A self-organizing neural network is proposed which is inherently a fuzzy inference system with the capability of learning fuzzy rules from data. The learning strategy consists of two phases: a self-organizing clustering to establish the structure of the network as well as the initial values of its parameters and a supervised learning phase for optimal adjustment of these parameters. After learning, the network encodes in its structure the essential design parameters of a fuzzy system. An example is given to illustrate the characteristics and capabilities of the proposed network.