diff --git a/doc/source/components.rst b/doc/source/components.rst
index fad7d32..321674d 100644
--- a/doc/source/components.rst
+++ b/doc/source/components.rst
@@ -675,6 +675,55 @@ Covariances
 ^^^^^^^^^^^
 
 :py:class:`imagine.observables.Covariances <imagine.observables.observable_dict.Covariances>`
+are a special type of
+:py:class:`ObservableDict <imagine.observables.observable_dict.ObservableDict>`
+which stores the set of covariance matrices associated with set of observables.
+Its behavour is similar to the
+:py:class:`Measurements <imagine.observables.observable_dict.Measurements>`,
+the main difference being that the :py:data:`global_data` attribute returns a
+(N,N) covariance matrix instead of (1,N)-array.
+
+In practice, one rarely needs to initialize or work with an independent
+:py:obj:`Covariances <imagine.observables.observable_dict.Covariances>` object.
+This is because a `Covariances` object is *automatically generated* when one
+initializes a :py:class:`Measurements <imagine.observables.observable_dict.Measurements>`
+a :py:obj:`Dataset <imagine.observables.dataset.Dataset>`, and stored in the
+`Measurements` object
+:py:data:`cov <imagine.observables.observable_dict.Measurements.cov>` attribute::
+
+  # The covariances associated with a measurements object
+  covariances = measurements.cov
+
+When the original dataset only contained *variance* information (e.g. specified
+using the :py:data:`error` keyword when preparing a :py:obj:`Dataset <imagine.observables.dataset.Dataset>`) the full covariance matrix is *not*
+constructed on initialization. Instead, the variance alone is stored internally
+and the covariance matrix construction is delayed until the first time the
+properties :py:data:`global_data` or :py:data:`data`
+are requested. If :py:data:`imagine.rc['distributed_arrays'] <imagine.tools.config>`
+is set to :py:data:`True` (not the default),
+the covariance matrix is spread among the available MPI processes.
+
+As many observational datasets are very large (i.e. very large N), and only
+contain known variance information, it often unnecessary (or simply impractical)
+to operate with full (sparse) covariance matrices. Instead, one can access
+the variance stored in the `Covariances` object using the :py:data:`var`
+property::
+
+  # An Observable object containing covariance information for a given key
+  cov_observable = covariances[('sync', 30, 32, 'I')]
+  # Reads the N-array containing the variances
+  variances = cov_observable.var
+  # Reads the (N,N)-array containing the covariance matrix
+  # (constructing it, if necessary)
+  cov_matrix = cov_observable.global_data
+
+Thus, when working with large datasets some care should be take when choosing
+(or designining) the :py:class:`Likelihood <imagine.likelihoods.likelihood>`
+class which will manipulate the `Covariances` object: some of the likelihoods
+(e.g. :py:class:`EnsembleLikelihoodDiagonal <imagine.likelihoods.ensemble_likelihood.EnsembleLikelihoodDiagonal>`)
+only access the `var` attribute, keeping the RAM memory usage under control
+even in the case of very large maps, while others will try construct the full
+covariance matrix, potentially leading out-of-memory errors.
 
 
 
@@ -686,7 +735,8 @@ Masks
 ^^^^^
 
 :py:class:`imagine.observables.Masks <imagine.observables.observable_dict.Masks>`
-
+which stores masks which can be applied to HEALPix maps. The mask itself is
+specified in the form of an array of zeros and ones.
 
 .. _Simulators: