Association rules are useful to discover relationships, which are mostly hidden, between the different items in large datasets. Symbolic models are the principal tools to extract association rules. This basic technique is time-consuming, and it generates a big number of associated rules. To overcome this drawback, we suggest a new method, called MARC, to extract the more important association rules of two important levels: Type I, and Type II. This approach relies on a multi-topographic unsupervised neural network model as well as clustering quality measures that evaluate the success of a given numerical classification model to behave as a natural symbolic model.