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Accurate time transfer by time of flight measurements via diffuse reflections on passive orbiting space debris targets requires a selection of suitable objects out of a large catalogue of debris items. In this paper, we report on our development of an automatic classification system of space objects based on photometric observations of sun illuminated satellite and debris items from the Mini–Mega TORTORA (MMT) system observation data base by a deep learning algorithm. A deep neural network model based on a convolutional long short-term memory network has been designed to identify four different object categories with a test accuracy of over 85%. The method is also suitable for an automated analysis of the temporal evolution of the orbit motion of specific space objects. • Neural networks exploit a single observable to classify many space objects. • Reliable space object classification is achieved where rule based systems fail. • Satellite or space debris orbit anomalies are automatically detected. • A body of thousands of different objects can be monitored continuously. [ABSTRACT FROM AUTHOR]