diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 49d755ae1954..1be58211442d 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6234,26 +6234,34 @@ def _normalize_input(inp, ename='input'):
 
             # adopted from adjust_x/ylim part of the bar method
             if orientation == 'horizontal':
-                xmin0 = max(_saved_bounds[0]*0.9, minimum)
-                xmax = self.dataLim.intervalx[1]
-                for m in n:
-                    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) if not input_empty else minimum
-                xmin = min(xmin0, xmin)
-                self.dataLim.intervalx = (xmin, xmax)
+                if normed:
+                    self.set_autoscalex_on(True)
+                else:
+                    xmin0 = max(_saved_bounds[0]*0.9, minimum)
+                    xmax = self.dataLim.intervalx[1]
+                    for m in n:
+                        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) if not input_empty else minimum
+                    xmin = min(xmin0, xmin)
+                    self.dataLim.intervalx = (xmin, xmax)
             elif orientation == 'vertical':
-                ymin0 = max(_saved_bounds[1]*0.9, minimum)
-                ymax = self.dataLim.intervaly[1]
-
-                for m in n:
-                    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) if not input_empty else minimum
-                ymin = min(ymin0, ymin)
-                self.dataLim.intervaly = (ymin, ymax)
+                 # If norm, autoscale axis
+                if normed:
+                    self.set_autoscaley_on(True)
+                else:
+                    ymin0 = max(_saved_bounds[1]*0.9, minimum)
+                    ymax = self.dataLim.intervaly[1]
+                    for m in n:
+                        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) if not input_empty else minimum
+                    ymin = min(ymin0, ymin)
+                    self.dataLim.intervaly = (ymin, ymax)
 
         if label is None:
             labels = [None]