A constructive algorithm for processing and identifying events of arbitrary nature is proposed. Ideologically, this algorithm is based on combination of two known approaches: fuzzy sets theory (the fact that the event is “typical” or “untypical” is determined by the value of its membership degree) and the method of potential functions (the metric properties of events are determined with the help of a symmetric nonnegative kernel given by one or another method). From the theoretical point of view, this is an adaptive learning algorithm that makes it possible to identify the analyzed events, and, from the practical point of view, this is an algorithm that gives an opportunity to estimate simultaneously the degree of “typicality”, to calcu late the coordinates, to provide computer visualization of these events. The role of events is played by ele ments of a sequence of output signals of an arbitrary discrete stochastic dynamic system. In addition, the esti mation of the “typicality” of events can be considered as the quantitative analysis of the studied system, while the visualization can be considered as its qualitative analysis.