This paper presents a new approach whose aim is to extent the scope of numerical models by providing them with knowledge extraction capabilities. The basic model which is considered in this paper is a multi-topographic neural network model. The powerful features of this model are its generalization mechanism and its mechanism of communication between topographies. These two mechanisms allow rule extraction to be performed whenever a single viewpoint or multiple viewpoints on the same data are considered. The association rule extraction is itself based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice.