diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index bf6e70c15eb3..30b5ec0078b0 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -5871,7 +5871,7 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
         Parameters
         ----------
         x : (n,) array or sequence of (n,) arrays
-            Input values, this takes either a single array or a sequency of
+            Input values, this takes either a single array or a sequence of
             arrays which are not required to be of the same length
 
         bins : integer or array_like or 'auto', optional
@@ -6042,8 +6042,8 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
 
         """
         def _normalize_input(inp, ename='input'):
-            """Normalize 1 or 2d input into list of np.ndarray or
-            a single 2D np.ndarray.
+            """Normalize 1 or 2d input into a list of one or more np.ndarrays
+            which have potentially different lengths.
 
             Parameters
             ----------
@@ -6054,25 +6054,30 @@ def _normalize_input(inp, ename='input'):
             """
             if (isinstance(x, np.ndarray) or
                     not iterable(cbook.safe_first_element(inp))):
-                # TODO: support masked arrays;
-                inp = np.asarray(inp)
+
+                inp = cbook.safe_masked_invalid(inp)
                 if inp.ndim == 2:
                     # 2-D input with columns as datasets; switch to rows
                     inp = inp.T
+
+                    if inp.shape[1] < inp.shape[0]:
+                        warnings.warn(
+                            '2D hist input should be nsamples x nvariables;\n '
+                            'this looks transposed '
+                            '(shape is %d x %d)' % inp.shape[::-1])
+
+                    # Change to a list of arrays
+                    inp = [inp[i, :] for i in range(inp.shape[0])]
+
                 elif inp.ndim == 1:
-                    # new view, single row
-                    inp = inp.reshape(1, inp.shape[0])
+                    # Change to a list with a single array
+                    inp = [inp.reshape(1, inp.shape[0])]
                 else:
                     raise ValueError(
                         "{ename} must be 1D or 2D".format(ename=ename))
-                if inp.shape[1] < inp.shape[0]:
-                    warnings.warn(
-                        '2D hist input should be nsamples x nvariables;\n '
-                        'this looks transposed '
-                        '(shape is %d x %d)' % inp.shape[::-1])
             else:
-                # multiple hist with data of different length
-                inp = [np.asarray(xi) for xi in inp]
+                # Change to a list of arrays
+                inp = [cbook.safe_masked_invalid(arr) for arr in inp]
 
             return inp
 
@@ -6117,23 +6122,23 @@ def _normalize_input(inp, ename='input'):
         binsgiven = (cbook.iterable(bins) or bin_range is not None)
 
         # basic input validation
-        flat = np.ravel(x)
-
-        input_empty = len(flat) == 0
+        input_empty = len(np.ravel(x)) == 0
 
-        # Massage 'x' for processing.
+        # Massage shape of 'x' to be a list of arrays
         if input_empty:
-            x = np.array([[]])
+            x = [np.array([[]])]
         else:
             x = _normalize_input(x, 'x')
         nx = len(x)  # number of datasets
 
-        # We need to do to 'weights' what was done to 'x'
-        if weights is not None:
-            w = _normalize_input(weights, 'weights')
-        else:
+        # Massage shape of 'weights' to be a list, where each element
+        # weights[i] is either None or an array with the same shape as x[i]
+        if weights is None:
             w = [None]*nx
+        else:
+            w = _normalize_input(weights, 'weights')
 
+        # Comparing shape of weights vs. x
         if len(w) != nx:
             raise ValueError('weights should have the same shape as x')
 
@@ -6142,6 +6147,16 @@ def _normalize_input(inp, ename='input'):
                 raise ValueError(
                     'weights should have the same shape as x')
 
+        # Combine the masks from x[i] and w[i] (if applicable) into a single
+        # mask and apply it to both.
+        if not input_empty:
+            for i in range(len(x)):
+                mask_i = x[i].mask
+                if w[i] is not None:
+                    mask_i = mask_i | w[i].mask
+                    w[i] = np.ma.masked_array(w[i], mask=mask_i).compressed()
+                x[i] = np.ma.masked_array(x[i], mask=mask_i).compressed()
+
         if color is None:
             color = [self._get_lines.get_next_color() for i in xrange(nx)]
         else: