This thesis aims to apply Convolutional Neural Networks (CNNs) to data generated by sensors to predict the quality of Resistance Spot Welding (RSW). RSW quality assessment was mainly based on destructive testing and manual inspections. These methods are expensive and not manageable for the real-time analysis required by an Industry 4.0 eco-system. Recent research has shifted toward machine learning and computational methods, which offer non-invasive and real-time evaluations, even though challenges persist in their application due to data limitations. In this study, we investigate the feasibility of using CNNs fed with the matrix generated by multiple sensors during the welding process with a complete black-box approach.