The global exponential stability of the equilibrium point of generalized delayed bidirectional associative memory (DBAM) neural networks with impulse effects was studied using the Lyapunov function and a 2D Hanalaytype inequality. Several results characterized the aggregated effects of impulses and the dynamic properties of the impulsefree DBAM on the exponential stability of the DBAM under consideration. It is shown that impulsive DBAM will preserve the global exponential stability of the impulsefree DBAM even if the impulses have enlarging effects on the states of neurons. The effectiveness of the theoretical results was validated by a numerical example.