The general architecture proposed improves both accuracy and comprehensibility by taking advantage of the special properties and dealing with the specific problems of temporal classification. An implementation of this architecture, called TClass, is applied to several learning tasks. It proved to be more accurate than a naïve approach, competitive with hand-crafted solutions and in some cases showed better performance than complex feature extraction techniques, while still providing useful descriptions of learnt concepts.