The geomorphometric indexes have been widely used for separation of surface landform features in the geomorphology science over the past decades. In this study, Multilayer Perceptron Neural Network (MPNN) was used to provide karstic landform classification. To that regard, initially, geomorphometric indicators were extracted from Digital Elevation Model (DEM), and then these indexes were used as neurons of input layer in artificial neural network. Furthermore, the box plots were applied to analyze the relationship between karstic landforms (such as dolines, hills, karstic plains, karstic valley and headland) and geomorphometric indexes. The results showed that 34, 6.9, 1.07, 48.5, 9.51 percent of the studying area are spatially covered by valleys, plains, dolines, highlands and hills respectively. It has also been found that the optimal structure of artificial neural networks for classification of landform is model No. 12-9-1 by having the learning rate 0.1 and 87.18 percent of determination coefficient. Also, it should be noted that the accuracy of the innovative method for classification of karstic landform is 90.58 percent. The analysis revealed that variations in geomorphometric indexes are very visible in the landform of hills, highlands and karstic valleys, whereas there are slightly overlapping in the plains and dolines.