Classification has been among the most typical computational problems in the last decades. In this paper, a new filtering network is proposed for data classification that is derived from radial base function networks (RBFNs), based on a simple observation about the nature of the classic RBFNs. According to that observation, the hidden layer of the network can be viewed as a fuzzy system, which compares the input data to the data stored in each neuron, computing the similarity between them. The output layer of the RBFN is modified in order to make it more effective in certain fuzzy decision-making systems. The training of the neurons is solved by a clustering step, for which a novel clustering method is proposed. Experimental results are also presented to show the efficiency of the approach.