diff --git a/doc/api/next_api_changes/2019-02-07-AH.rst b/doc/api/next_api_changes/2019-02-07-AH.rst
new file mode 100644
index 000000000000..9681457e9a04
--- /dev/null
+++ b/doc/api/next_api_changes/2019-02-07-AH.rst
@@ -0,0 +1,6 @@
+Modify output of Axes.hist when input consists of multiple empty lists
+``````````````````````````````````````````````````````````````````````
+
+Input that consists of multiple empty lists will now return a list of histogram
+values for each one of the lists. For example, an input of ``[[],[]]`` will
+return 2 lists of histogram values. Previously, a single list was returned.
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index d471c3815f7c..942b5652003c 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6625,10 +6625,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
         # basic input validation
         input_empty = np.size(x) == 0
         # Massage 'x' for processing.
-        if input_empty:
-            x = [np.array([])]
-        else:
-            x = cbook._reshape_2D(x, 'x')
+        x = cbook._reshape_2D(x, 'x')
         nx = len(x)  # number of datasets
 
         # Process unit information
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 1a848692907e..cea0f2291185 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1665,6 +1665,20 @@ def test_hist_datetime_datasets():
     ax.hist(data, stacked=False)
 
 
+@pytest.mark.parametrize('data, expected_number_of_hists',
+                         [([], 1),
+                          ([[]], 1),
+                          ([[], []], 2)])
+def test_hist_with_empty_input(data, expected_number_of_hists):
+    hists, _, _ = plt.hist(data)
+    hists = np.asarray(hists)
+
+    if hists.ndim == 1:
+        assert 1 == expected_number_of_hists
+    else:
+        assert hists.shape[0] == expected_number_of_hists
+
+
 def contour_dat():
     x = np.linspace(-3, 5, 150)
     y = np.linspace(-3, 5, 120)