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In general regression neural networks (GRNN), one drawback is that the number of training vectors is proportional to the number of hidden nodes, thus a large number of training vectors produce a larger architecture, which is a major disadvantage for many applications. In this paper we proposed an efficient clustering technique referred to as 'similarity index fuzzy c-means clustering'. This technique uses the conventional fuzzy c-means clustering preceded by a technique based on similarity indexing to automatically cluster input data which are relevant to the system. The technique employs a one-pass similarity measures on the data to calculate the similarity index. This index indicates the degree of similarity in which data is clustered. Similar data then undergoes fuzzy c-means iterative process to determine their cluster centers. We applied the technique for system identification and modeling and found the results to be encouraging and efficient.