This research examines the problems of automatic scientific articles classification according to Universal Decimal Classifier. To reveal the structure of the train data its visualization was obtained using the recursive feature elimination algorithm. Further; the study provides a comparison of TF-IDF and Weirdness – two statistic-based metrics of keyword significance. The most efficient classification methods are explained: cosine similarity method, naïve Bayesian classifier and artificial neural network. This research explores the most effective for text categorization structure of the multi-layer perceptron and derives appropriate conclusions.