The interaction of gamma rays and cosmic rays with the Earth’s atmosphere initiate air showers that, in turn, induce the emission of Cherenkov photons detectable by ground-based Imaging Atmospheric Cherenkov Telescopes (IACTs). Any data analysis software for gamma-ray astronomy with IACTs requires an essential component to discriminate the nature of the primary particle, as well as to reconstruct its energy and arrival direction. In this field, the standard reconstruction approach is to use supervised machine learning techniques, mostly based on decision trees or Random Forest, which build models by training on simulated data using image and stereoscopic parameters as input features. This approach can be overcome by deep learning techniques, directly operating on pixelated camera images recorded by the array telescopes as input to models. In this way, all available information per each shower image can potentially be exploited for reconstruction, without relying solely on derived parameters. We evaluated some deep learning techniques on Monte Carlo simulated data of the ASTRI Mini-Array, an array of nine dual-mirror 4-m class IACTs under deployment at the Observatorio del Teide (Tenerife, Spain), sensitive to gamma-ray radiation in the 1–200 TeV energy range. In this contribution we present how deep learning algorithms such as convolutional neural networks can be used to reconstruct events acquired by the ASTRI Mini-Array; we will first describe the analysis work flow and introduce the architectures, and then compare the performance obtained with the new reconstruction methods with that of standard method.