The main goal of this diploma thesis is to explain how to use a meta-heuristic named Artificial Bee Colony to solve the problem of feature selection in the field of machine learning. The Procedure is described and applied to some standard data sets which are usually used in similar researches. First two chapters describe the theoretical side of the topic, and second two chapters represent implementation and results. At the beginning of the first chapter, the problem of feature selection is described. After that, used terms, models, and conceptions of machine learning are depicted, and also some standard methods for solving the mentioned problem. In the second chapter, basic terms related to meta-heuristics are defined. Artificial Bee Colony is explained afterward. The third chapter presents data sets that are used, data preprocessing, adjustment, and application of the bee algorithm. It is also presented which other methods for feature selection are applied to data sets. Results of the bee algorithm, comparison to results of other methods, and listed parameters are contained in the fourth chapter. In the end, it is concluded that Binary Artificial Bee Colony algorithm proved to be successful in solving the described problem.