In the process of transition to Industry 4.0, the importance of applying cutting-edge technologies such as machine learning and artificial intelligence to replace human operators in industrial processes is explained by the need to automate industrial production processes. Replacing qualified human experts with artificial neural networks opens up a lot of possibilities for the implementation of new methods of industrial process automation. The problem of industrial process automation is quite complex because the decision-making process of the human expert is accompanied by uncertainty. Artificial neural networks represent one of the basic branches of artificial intelligence. At the moment, they are used in various fields to solve problems for which classical methods are unable to provide practical solutions. Thus, the problem of developing and training artificial neural networks for solving industrial process automation problems acquires major importance in the design of artificial intelligence systems. The training process directly depends on the data set on the basis of which the neural network is designed.