Abstract This paper proposes the architecture of a convolutional neural network that creates a neural network system for recognizing objects in images using our own approach to classification using a hierarchical classifier. The architecture will be assigned to find the optimal solution to the problem for many sets of image data and, unlike existing approaches, will have high performance indicators without losing the number of parameters during recognition, and most importantly, the best value of object recognition accuracy compared to existing models of convolutional neural networks. The main attention is paid to the approach to training such a network and conducting experiments on the generated samples of various datasets using graphic processing units (GPUs). uthors. Published by Elsevier B.V. Keywords: neural network machine learning; image recognition during shooting; optimization algorithm for convolutional neural networks; hierarchical classifier