Systems classification by bootstrap filter

State estimation problem together with systems classification is solved. The novel idea is to represent all conditional probability density functions (c.p.d.f.) as a set of random samples. Usual approach is to represent c.p.d.f. as a function over the state space. Using large number of samples an exact equivalent representation of c.p.d.f. is obtained. From this samples the estimates of moments like mean and covariances can be obtained. In [1] is described bootstrap filter for updating these samples for discrete time system. In this way any nonlinearity and nonnormality of noise can be handled. The contribution of this article is to use this approach to systems classification.