The current paper demonstrates the effective capabilities of deep neural networks in solving the problem of identification of constellations from a photo of the sky in conditions of a priori uncertainty, incomplete observability and stochastic disturbances. The quality of solution 0,927 by metric F1 is obtained. In order to achieve the result, the original ResNet-like architecture of the convolution neural network was synthesized; statistical analysis of the dataset was carried out, the function of losses and strategy of neural network training were developed, and an accurate criterion of constellation observability in the image was formed. The observation of noise influence on the quality and stability of solutions was carried out.