Landscape classification is a demanding task,mainly because of andscape’s holistic nature. Landscape experts are able to intuitively recognize landscape units which, regarding the morphological and physical factors, prove unified and homogeneous. But with such »gestalt « perception only those landscape units can be recognized that differ from the wider area and are evidently distinguishable. The classification of usually smooth and fuzzy passages between them often presents a problem. Another frequent approach to landscape classification is a clear and repeatable parametric approach. Landscape units are defined by simple combination of physical factors. But landscape is too complex system for such simplification. In search of new methods of landscape classification the usability of artificial neural networks was tested. Their application was based on landscape samples that experts identified in the area of »Karst landscape of the interior of Slovenia « and classified into 7 landscape types. Based on their known locations and on their spatial characteristics, artificial neural networks were able to learn the general rules of spatial occurrence of landscape types and use them for typological classification of the remaining territory. Artificial neural networks proved to be a very useful tool, mainly because of their ability to learn and to generalize.