The SIMBAD (Similarity-based Pattern Analysis and Recognition) Project aimed at undertaking a thorough study of several aspects of purely similarity-based pattern analysis and recognition methods, from the theoretical, computational, and applicative perspective. The project considered both supervised and unsupervised learning paradigms, generative and discriminative models, and with interests ranging from purely theoretical problems to real-world practical applications.