Artificial Intelligence is emerging as a disruptive technological field, with the potential to revolutionize the industrial world. In this context, Machine Learning is generally referred to as a branch of techniques which are designed to allow machines to learn and extract patterns from data. Among these, the so-called Deep Learning models have received particular interest for their potential to reduce human operations in several different applications. In this thesis, the attention is focused on the development and applicability of these models to image recognition and classification for industrial automation. In particular, the considered test case focuses on the automation of the quality control of brake calipers, from an important automotive player, in a typical production line of a company specialized in industrial automation.