diff --git a/lib/matplotlib/axes.py b/lib/matplotlib/axes.py
index d50928e60466..db671e7967c2 100644
--- a/lib/matplotlib/axes.py
+++ b/lib/matplotlib/axes.py
@@ -8245,6 +8245,17 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
         if histtype == 'barstacked' and not stacked:
             stacked = True
 
+        # Check whether bins or range are given explicitly.
+        binsgiven = (cbook.iterable(bins) or bin_range is not None)
+
+        # basic input validation
+        flat = np.ravel(x)
+        if len(flat) == 0:
+            raise ValueError("x must have at least one data point")
+        elif len(flat) == 1 and not binsgiven:
+            raise ValueError(
+                "x has only one data point. bins or range kwarg must be given")
+
         # Massage 'x' for processing.
         # NOTE: Be sure any changes here is also done below to 'weights'
         if isinstance(x, np.ndarray) or not iterable(x[0]):
@@ -8299,10 +8310,6 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
         # Save the datalimits for the same reason:
         _saved_bounds = self.dataLim.bounds
 
-        # Check whether bins or range are given explicitly. In that
-        # case use those values for autoscaling.
-        binsgiven = (cbook.iterable(bins) or bin_range is not None)
-
         # If bins are not specified either explicitly or via range,
         # we need to figure out the range required for all datasets,
         # and supply that to np.histogram.
@@ -8310,8 +8317,9 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
             xmin = np.inf
             xmax = -np.inf
             for xi in x:
-                xmin = min(xmin, xi.min())
-                xmax = max(xmax, xi.max())
+                if len(xi) > 0:
+                    xmin = min(xmin, xi.min())
+                    xmax = max(xmax, xi.max())
             bin_range = (xmin, xmax)
 
         #hist_kwargs = dict(range=range, normed=bool(normed))
@@ -8503,7 +8511,8 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
                 xmin0 = max(_saved_bounds[0]*0.9, minimum)
                 xmax = self.dataLim.intervalx[1]
                 for m in n:
-                    xmin = np.amin(m[m != 0]) # filter out the 0 height bins
+                    if np.sum(m) > 0:  # make sure there are counts
+                        xmin = np.amin(m[m != 0]) # filter out the 0 height bins
                 xmin = max(xmin*0.9, minimum)
                 xmin = min(xmin0, xmin)
                 self.dataLim.intervalx = (xmin, xmax)
@@ -8511,7 +8520,8 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
                 ymin0 = max(_saved_bounds[1]*0.9, minimum)
                 ymax = self.dataLim.intervaly[1]
                 for m in n:
-                    ymin = np.amin(m[m != 0]) # filter out the 0 height bins
+                    if np.sum(m) > 0:  # make sure there are counts
+                        ymin = np.amin(m[m != 0]) # filter out the 0 height bins
                 ymin = max(ymin*0.9, minimum)
                 ymin = min(ymin0, ymin)
                 self.dataLim.intervaly = (ymin, ymax)
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 4b166bcd5c94..3919c7bd034b 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1162,6 +1162,12 @@ def test_hist_stacked_bar():
     ax.hist(d, bins=10, histtype='barstacked', align='mid', color=colors, label=labels)
     ax.legend(loc='upper right', bbox_to_anchor = (1.0, 1.0), ncol=1)
 
+@cleanup
+def test_hist_emptydata():
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    ax.hist([[], range(10), range(10)], histtype="step")
+
 @image_comparison(baseline_images=['transparent_markers'], remove_text=True)
 def test_transparent_markers():
     np.random.seed(0)