In the challenging scenario of Automatic Electronic Testing of Printed Circuit Boards (PCBs), a crucial role is played by the recognition of specific electronic components, such as resistors, inductors, capacitors, and others. This task usually requires an extensive amount of manual labor, necessitating the presence of a domain expert. The great heterogeneity of PCBs and the highly variable environmental conditions under which images are collected within a corporate production mechanism also demand high precision and significant time. The objective of this thesis is to demonstrate the use of artificial intelligence (AI) algorithms, particularly machine learning (ML) and deep learning (DL) ones, to perform the task of image recognition, determining the position and nature of various components within PCBs.