In this paper, a class of stochastic fuzzy Cohen-Grossberg neural networks with time-varying delays is considered. By utilizing Razumikhin technique and constructing new delay differential inequalities, some new suffcient con ditions ensuring the mean-square exponentially input-to-state stability prop- erty of delayed network systems are obtained. A numerical example is given to illustrate the effciency of the derived results.
