Learning Classifier Systems which build anticipations of the expected states following their actions are a focus of current research. This paper presents a mechanism by which to create learning classifier systems of this type, here using accuracybased fitness. In particular, we highlight the supervised learning nature of the anticipatory task and amend each rule of the system with a traditional artificial neural network. The system is described and shown able to perform well in a number of well-known maze tasks.