Presented in this paper is the idea of GIS layers semantic recognition methodology. The aim was to evaluate a possibility of GIS layer recognition based on spatial analysis and performance tests which validate proposed methodology. The final interest was to develop a GIS layer classifier and evaluate its function for independent data set. In my approach to the classification of the GIS data layers I use methods based on the nearest neighbor and histogram of the distance matrix. The reasons for such a solution are in good complexity of the spatial data description and in implementation of these algorithms under statistics software. In the range of the experiment tests I developed methodology for classification and I verified that it is possible to recognize the spatial layer via spatial statistic. Then I developed the classifier based on the Kohonen's Self Organization Maps and evaluated it on a test set. All the executed tests under artificial spatial data and real GIS data show that the proposed methodology is fully relevant and forms a basis for successful use in practical applications. All executed classification models showed that the proposal methodology could directly recognize the GIS layer, as for layers with similar spatial characteristic they recognize only a class of layers. For complete recognition it is necessary to add other information about layers.