Classification of bloodstain patterns at the crime scene is a crucial task in the context of forensic investigations. Traditionally, this task is carried out by forensic experts by applying, unfortunately, time-consuming procedures and subjective judgments. For this reason, artificial intelligence techniques have recently been applied to make this task faster and less subjective. This work presents, for the first time, the application of different deep learning techniques for facing the classification of bloodstain patterns as a multi-class problem. As shown in the experimental session, where a dataset created from scratch has been used, deep learning show optimal performance in facing multi-class classification of bloodstains at the crime scene.