The paper presents a method of statistical analysis of multidimensional data by means of the PCA transformation (Principal Components Analysis), carried out by means of a neuron network. The PCA transformation, connected with the Karhunen-Loeve transformation is used for processing signals treated as stochastic processes. The method discussed in the paper, enables the reduction of input data on the basis of specified independent principal components with respect to their significance,

The paper addresses the problem of solving overdetermined systems of linear equations by means of methods of robust estimations, which eliminate the effect of outliers on the estimation results. The process of estimating a vector of parameters was accomplished by means of circular in structure neural networks. Formulating the problem in the aspect of a method for estimating parameters requires formulating an energy function (objective function) whose form was modified by means of a determined weighting function. In the final part of the paper the effectiveness of the methods described was evaluated in terms of controlling and diagnosing a geodetic observation system. The article is merely an introduction to a broadly understood problem of geodetic uses of robust estimators.