A variety of methods exists for electroencephalographic (EEG) signals classification. In this paper, we briefly review selected methods developed for such a purpose. First, a short description of the EEG signal characteristics is provided. Then, a comparison between the selected EEG signal classification methods, based on the overview of research studies on this topic, is presented. Examples of methods included in the study are: Artificial Neural Networks, Support Vector Machines, Fuzzy or k-Means Clustering. Similarities and differences between all considered methods of an automatic EEG signal classification with a focus on consecutive stages of such a process are reviewed. Examples of EEG classification, considering various types of usage and target applications along with their effectiveness, are also shown.