In this paper,we propose new delay independent and dependent H ∞ stability conditions for delayed neural networks with external disturbances and norm-bounded uncertainties.These conditions are presented to not only guarantee the asymptotical stability but also reduce the effect of external disturbance to an H_∞ norm constraint. The proposed conditions are represented by linear matrix inequalities (LMIs).Optimal H_∞ norm bounds are obtained easily by solving convex problems in terms of LMIs.The applicability of these conditions is illustrated by numerical examples. In this paper, we propose new delay independent and dependent H ∞ stability conditions for delayed neural networks with external disturbances and norm-bounded uncertainties. These conditions are presented to not only guarantee the asymptotical stability but also reduce the effect of external disturbance to an H_ ∞ norm constraint. The proposed conditions are represented by linear matrix inequalities (LMIs). Optimal H_∞ norm bounds are obtained easily by solving convex problems in terms of LMIs.The applicability of these conditions is illustrated by numerical examples.