In the work, current artifical intelligence methods are concisely presented: neural networks, fuzzy logic, genetic algorithms and expert systems. Structure of the designed intelligent system for material selection in tribological conditions is described detailedly, as well as the ways for improvement of its main inference engine component. The subengines of the main inference engine are also presented: conversation processing engine, associations processing engine, rules processing engine, criteria processing & material selection engine, engine for selection of surface modification processes. In experimental part of the work, concept for construction of the intelligent system for material selection is given, as well as the method how to present the experimental results and an overview of software tools. Mapping methods of the input-output data for training of neural networks and inference engines using fuzzy logic are presented. Procedure for creating of the time associative matrices which manage obtaining and explanation of the solution of the analyzed problem is presented. Optimizing of neural network using genetic algorithms is presented, as well as application of NeuroFuzzy model of fuzzy logic. An actual example illustrating operating principle of inference engine of the intelligent system for material selection is provided.