This paper presents a fuzzy-neural network that admits both numeric as well as linguistic inputs. Numeric inputs are fuzzified by input nodes upon presentation to the network. Fuzzy rule-based knowledge is translated directly into a network architecture. Connections in the network are represented by fuzzy sets: Input to hidden connections represent rule antecedents; hidden to output connections represent rule consequents. The novelty of the model lies in the method of activation spread in the network which is based on a fuzzy mutual subsethood measure. Rule (hidden) node activations are computed as a fuzzy inner product. For a given numeric or fuzzy input, numeric outputs are computed using volume based defuzzification. A supervised learning procedure based on gradient descent is employed to train the network. The model has a natural capability for inference, function approximation, and classification and is versatile in that it can handle numeric and fuzzy inputs simultaneously. In this paper, we focus on the classification ability of the model and demonstrate its performance on three benchmark classification problems: the Iris data set, Ripley's synthetic two class problem, and Pal and Mitra's Telegu vowel data. Results show that the classifier performs at par or better than various other techniques.