Microscopic wear debris is produced in all machines containing moving parts in contact. The debris (particles), transported by a lubricant from wear sites; carry important information relating to the condition of the machinery. This information is classified by compositional and six morphological attributes of particle size, shape, edge details, color, thickness ratio, and surface texture. The paper describes an automated system for surface features recognition of wear particles by using artificial neural networks. The aim is to classify these particles according to their morphological attributes and by using the information obtained, to predict wear failure modes in engines and other machinery. This approach will enable the manufacturing industry to improve quality, productivity and economy. The procedure reported in this paper is based on gray level cooccurrence matrices, that are used to train a feed-forward neural network classifier in order to distinguish among seven different patterns of wear particles. The patterns are: smooth, rough, striations, holes, pitted, cracked, and serrated. An accuracy classification rate of 94.6% has been achieved and is shown by a confusion matrix.