Wear mechanism severity analysis constitutes one of the principal strategies for selecting more wear-resistant materials. A machine learning model was developed to characterize and recognize wear mechanisms and severityin worn surfaces of industrial equipment. From a set of images of worn surfaces of transfer chute linings, two different datasets were employed to search independently among five wear mechanisms and four levels of wear severity.The proposed approach was successful in recognizing and mapping wear mechanisms and determining their severity levels, as confirmed by the high values of metrics obtained using the deep features.