Accurate classification of pottery vessels is a key aspect in several archaeological inquiries, including documentation of changes in style and ornaments, inference of chronological and ethnic groups, trading routes analyses, and many other matters. We present an unsupervised method for automatic feature extraction and classification of wheel-made vessels. A convolutional neural network was trained with a profile image database from Iberian wheel made pottery vessels found in the upper valley of the Guadalquivir River (Spain). During the design of the model, data augmentation and regularization techniques were implemented to obtain better generalization outcomes. The resulting model is able to provide classification on profile images automatically, with an accuracy mean score of 0.9013. Such computation methods will enhance and complement research on characterization and classification of pottery assemblages based on fragments.