When we generate association rules, continuous attributes have to be discretized into intervals while our knowledge representation is not always based on such discretiztion. For example, we usually use some linguistic terms (e.g., young, middle age, and old) for dividing our ages into some fuzzy categories. In this paper, we describe the extraction of linguistic association rules and examine the performance of extracted rules. First we modify the definitions of the two basic measures (i.e., confidence and support) of association rules for extracting linguistic association rules. The main difference between standard and linguistics association rules is the discretiztion of continuous attributes. We divide the domain interval of each attribute into some Fuzzy discretiztion with standard on-fuzzy discretiztion Through computer simulations on a pattern classification problem with many continuous attributes. The classification performance of extracted rules on unseen test patterns is examined under various conditions. Simulation results show that linguistic association rules with rule weights have high generalization ability even when the domain of each continuous attribute is homogeneously partitioned.