In their daily work, engineers in the Failure Analysis (FA) laboratory generate numerous documents reporting all their tasks, findings, and conclusions regarding every device they are handled. This data stores valuable knowledge for the laboratory that other experts can consult, however, the nature of it, as individual reports reporting concrete devices and their corresponding processes, makes it inefficient to consult for the human experts. In this context, the following paper proposes a Artificial Intelligence solution for the gathering of this FA knowledge stored in the numerous documents generated in the laboratory. Therefore, we have generated a dataset of FA reports along with their corresponding electrical signatures and physical failures in order to train different supervised classifiers. The results show that the models are able of capturing the patterns underlying the different jobs and predict the causes, showing slightly better results for the physical hypotheses.