Empirical dependencies are often used in various fields of geotechnics and civil engineering. The existing empirical formulas are mainly developed with the use of regression and multiple regression. Recently, another predictor is gaining more and more popularity - artificial neural networks. Artificial neural networks (ANNs) are one of the artificial intelligence methods relatively new to geotechnical science. This paper discusses the use of artificial neural networks to estimate the mechanical parameters of soils based on known physical characteristics. This problem has been of interest to geotechnical scientists for a long time, and some new correlations between mechanical and physical characteristics still appear. To develop this correlation a fully connected artificial neural network of direct propagation was used in the research. The neural network was trained on the data of laboratory tests of soil samples in the city of Novosibirsk, Russia. The article contains a description of the main features of correlations developing with artificial neural networks. As a result of this study, an artificial neural network was obtained that allows predicting the angle of friction and specific cohesion of clay soil with reasonable accuracy. The topology of the neural network is proposed, and the comparison of the estimation accuracy with the existing equations is carried out. According to the comparison of the results, it turned out that the ANN allows increasing the estimation accuracy of both parameters.