The theme of this work is application of artificial intelligence in optimization of manufacturing systems work. Modern manufacturing is based on application of techniques whose objective is improvement of current capability of manufacturing systems. Many researchers conducted in the World are based on possibilities of implementation artificial neural networks in manufacturing supervision systems. Application of genetic algorithms is possible in all areas, where is system optimization needed; in which conventional optimization methods doesn´t provide satisfactory result. Genetic algorithms have property of function global extreme searching, which in some conventional optimization methods isn´t possible. Base of this work is comparison of conventional optimization techniques and optimization techniques based on artificial intelligence, and their implementation in manufacturing systems. In second part of this work, mathematical models of manufacturing processes are given as fundamental models on which are based conventional optimization methods. In part three are presented and described conventional approaches and optimization results of some manufacturing processes. Part three is meant to provide possibility to compare conventional optimization methods with optimization methods based on artificial intelligence. In part four is presented tool path optimization model based on genetic algorithms. Given that, the tool positioning time in some manufacturing processes is great, optimization enabled reducing of needed tool positioning time, and with that faster production. Part five represents concept of intelligent manufacturing system, with representation of all vital functioning parts of manufacturing system, and are given bases of manufacturing system automatization control. In part six are given research results obtained by application of artificial neural networks in tool wear monitoring systems, and in chapter seven is conclusion of this work.