The present diploma work investigates visualization of multidimensional data using multilayer neuron networks. The research comprised artificial neuron network (SAMANN) for non – linear projection, and visualization of multidimensional space data to smaller measurement space. The SAMANN network is trained by error backpropagation algorithm, that is, a multilayer perceptron is trained by using gradient descent rule for minimization of accumulated square error. Programme language helped creating a language for SAMANN network realization. Practical researches were carried out, that is, plane visualization of specific data sets complex (vectors). Dependence of visualization accuracy on number of iterations, training speed parameter and number of neurons in hidden layers was established. The following software was used for the work: Microsoft Visual Studio 6.00 C++ (for realization of SAMANN network) and Microsoft Excel 2002 (for visualization of the created programme).