We present the results of a numerical experiment aiming at explaining reasons for classification errors when using an automatic pattern-based terrain classifications algorithm proposed by Jasiewicz et. al. . We use composition of landform elements from incorrectly classified areas, and we use texture pattern from example areas to synthesize a new “terrain” which inherits properties from both sources. Using a new Pattern Analysis Toolbox (GeoPAT, [4,5]) we found that classification errors come from convergence of landscape properties: after replacing texture in misclassified areas with texture as indicated by an example area a new synthetic area shows higher degree of similarity to the landscape class from which it inherits texture. It allow to draw conclusion that short-range textural properties is that feature which at that moment best describes diversity of landscapes for automatic classifications.