Fuzzy classification is the task of partitioning a feature space into fuzzy classes. A learn-by-example mechanism is desirable to automate the construction process of a fuzzy classifier. In this paper we introduce a method of employing adaptive networks to solve a fuzzy classification problem. System parameters, such as the membership functions defined for each feature and the parameterized t-norms used to combine conjunctive conditions are calibrated with backpropagation. To explain this new approach, first we introduce the concept of adaptive networks and derive a supervised learning procedure based on a gradient descent algorithm to update the parameters in an adaptive network. Next we apply the proposed architecture to two problems: two-spiral classification and Iris categorization. From the experiment results, it is summarized that the adaptively adjusted classifier performs well on an Iris classification problem. The results are discussed from the viewpoint of feature selection.