A robust validity index for fuzzy c-means (FCM) algorithm is proposed in this paper. The purpose of fuzzy clustering is to partition a given set of training data into several different clusters that can then be modeled by fuzzy theory. The FCM algorithm has become the most widely used method in fuzzy clustering. Although, there are some successful applications of FCM have been proposed, a disadvantage of FCM is that the number of clusters must be predetermined. After clustering, it is often necessary to evaluate the fitness of the results obtained by FCM. Such assessment techniques are called cluster validity. In this paper, a new cluster validity index is proposed to evaluate the fitness of clusters obtained by FCM and four examples show the results of proposed index have good performances than other cluster validities.