During the adaptation process of the weights vector that occurs in the iteration presentation of the teaching vector, the Kohonen type neural network attempts to learn the structure of the data. Such a network can learn to recognise aggregates of input data occuring in the input data set regardless of the assumed criteria of similarity and the quantity of the data explored. Following identification of aggregates occurring in the data set, they can be named (labelled), and as a result the Kohonen network gains the ability to classify them in compliance with the inner logic included in the data set. The Kohonen type neural network can therefore be used for classification of data also when the output classes are not known (defined) in advance.