diff --git a/GPy/plotting/matplot_dep/models_plots.py b/GPy/plotting/matplot_dep/models_plots.py
index d72d2a3e..4ca4441e 100644
--- a/GPy/plotting/matplot_dep/models_plots.py
+++ b/GPy/plotting/matplot_dep/models_plots.py
@@ -56,10 +56,13 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
     if ax is None:
         fig = pb.figure(num=fignum)
         ax = fig.add_subplot(111)
-
-    X, Y = param_to_array(model.X, model.Y)
-    if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs(): X_variance = model.X_variance
-
+    
+    if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs(): 
+        X = model.X.mean
+        X_variance = param_to_array(model.X.variance)
+    else:
+        X = model.X
+    X, Y = param_to_array(X, model.Y)
     if hasattr(model, 'Z'): Z = param_to_array(model.Z)
 
     #work out what the inputs are for plotting (1D or 2D)
@@ -98,10 +101,10 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
 
 
         #add error bars for uncertain (if input uncertainty is being modelled)
-        #if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs():
-        #    ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(),
-        #                xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
-        #                ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
+        if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs():
+            ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(),
+                        xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
+                        ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
 
 
         #set the limits of the plot to some sensible values