This paper aims to classify electromagnetic compatibility (EMC) issues using autoencoders, a dimensionality reduction technique, and machine learning models. The process begins by generating EMC measurement data that closely reflects real-world measurements. The samples are then reduced using autoencoders and used as input for the machine-learning models. The results demonstrate that the machine learning techniques were able to accurately classify between the different EMC classes.