diff --git a/openmc/stats/univariate.py b/openmc/stats/univariate.py
index e0475bf78fb..556cc69e040 100644
--- a/openmc/stats/univariate.py
+++ b/openmc/stats/univariate.py
@@ -249,7 +249,7 @@ def from_xml_element(cls, elem: ET.Element):
     def merge(
         cls,
         dists: Sequence[Discrete],
-        probs: Sequence[int]
+        probs: Sequence[float]
     ):
         """Merge multiple discrete distributions into a single distribution
 
@@ -830,6 +830,36 @@ def from_xml_element(cls, elem: ET.Element):
         params = get_text(elem, 'parameters').split()
         return cls(*map(float, params))
 
+    @classmethod
+    def merge(
+        cls,
+        dists: Sequence[Normal],
+        probs: Sequence[float]
+    ):
+        """Merge multiple normal distributions into a single distribution
+
+        Parameters
+        ----------
+        dists : iterable of openmc.stats.Normal
+            Normal distributions to combine
+        probs : iterable of float
+            Probability of each distribution
+
+        Returns
+        -------
+        openmc.stats.Normal
+            Combined normal distribution
+
+        """
+        if len(dists) != len(probs):
+            raise ValueError("Number of distributions and probabilities must match.")
+        means = np.array([d.mean_value for d in dists])
+        stds = np.array([d.std_dev for d in dists])
+        probs = np.asarray(probs)
+        combined_mean = np.dot(probs,means)
+        combined_std = np.sqrt(np.dot(probs**2,stds**2))
+        return cls(combined_mean, combined_std)
+
 
 def muir(e0: float, m_rat: float, kt: float):
     """Generate a Muir energy spectrum
@@ -1324,6 +1354,59 @@ def from_xml_element(cls, elem: ET.Element):
 
         return cls(probability, distribution)
 
+    @classmethod
+    def merge(
+        cls,
+        dists: Sequence[Univariate],
+        probs: Sequence[float]
+    ):
+        """Merge multiple distributions into a single distribution
+
+        Parameters
+        ----------
+        dists : iterable of openmc.stats.Univariate
+            Univariate distributions to combine
+        probs : iterable of float
+            Probability of each distribution
+
+        Returns
+        -------
+        openmc.stats.Univariate
+            Combined distribution
+
+        """
+        if len(dists) != len(probs):
+            raise ValueError("Number of distributions and probabilities must match.")
+        
+        discrete = [[],[]]
+        normal = [[],[]]
+        others = [[],[]]
+        for p,d in zip(probs,dists):
+            if isinstance(d, Mixture):
+                iterator = zip(p * m.probability, m.distribution)
+            else:
+                iterator = [(p,d)]
+            for prob,dist in iterator:
+                if isinstance(dist,Discrete):
+                    discrete[0].append(dist)
+                    discrete[1].append(prob)  
+                elif isinstance(dist,Normal):
+                    normal[0].append(dist)
+                    normal[1].append(prob)
+                else:
+                    others[0].append(dist)
+                    others[1].append(prob)
+        if discrete[0]:
+            others[0].append(Discrete.merge(*discrete))
+            others[1].append(1.0)
+        if normal[0]:
+            others[0].append(Normal.merge(*normal))
+            others[1].append(1.0)
+        if others[1]==[1.0]:
+            return others[0][0]
+        else:
+            return cls(others[1], others[0])
+    
     def integral(self):
         """Return integral of the distribution
 
@@ -1406,8 +1489,8 @@ def combine_distributions(
     """Combine distributions with specified probabilities
 
     This function can be used to combine multiple instances of
-    :class:`~openmc.stats.Discrete` and `~openmc.stats.Tabular`. Multiple
-    discrete distributions are merged into a single distribution and the
+    :class:`~openmc.stats.Univariate`. Multiple
+    discrete or normal distributions are merged into a single distribution and the
     remainder of the distributions are put into a :class:`~openmc.stats.Mixture`
     distribution.
 
@@ -1421,32 +1504,4 @@ def combine_distributions(
         Probability (or intensity) of each distribution
 
     """
-    # Get copy of distribution list so as not to modify the argument
-    dist_list = deepcopy(dists)
-
-    # Get list of discrete/continuous distribution indices
-    discrete_index = [i for i, d in enumerate(dist_list) if isinstance(d, Discrete)]
-    cont_index = [i for i, d in enumerate(dist_list) if isinstance(d, Tabular)]
-
-    # Apply probabilites to continuous distributions
-    for i in cont_index:
-        dist = dist_list[i]
-        dist._p *= probs[i]
-
-    if discrete_index:
-        # Create combined discrete distribution
-        dist_discrete = [dist_list[i] for i in discrete_index]
-        discrete_probs = [probs[i] for i in discrete_index]
-        combined_dist = Discrete.merge(dist_discrete, discrete_probs)
-
-        # Replace multiple discrete distributions with merged
-        for idx in reversed(discrete_index):
-            dist_list.pop(idx)
-        dist_list.append(combined_dist)
-
-    # Combine discrete and continuous if present
-    if len(dist_list) > 1:
-        probs = [1.0]*len(dist_list)
-        dist_list[:] = [Mixture(probs, dist_list.copy())]
-
-    return dist_list[0]
+    return Mixture.merge(dists, probs)
diff --git a/tests/unit_tests/test_stats.py b/tests/unit_tests/test_stats.py
index 386181f34d2..9bb2a0955f6 100644
--- a/tests/unit_tests/test_stats.py
+++ b/tests/unit_tests/test_stats.py
@@ -444,6 +444,15 @@ def test_normal():
     assert_sample_mean(samples, mean)
 
 
+def test_combine_normal():
+    norm1=openmc.stats.Normal(mean_value=10, std_dev=1)
+    norm2=openmc.stats.Normal(mean_value=1, std_dev=1)
+    combined=openmc.stats.combine_distributions([norm1,norm2],probs=[0.1,0.9])
+    assert isinstance(combined, openmc.stats.Normal)
+    assert combined.mean_value == pytest.approx(1.9)
+    assert combined.std_dev**2 == pytest.approx(0.82)
+
+
 def test_muir():
     mean = 10.0
     mass = 5.0