This paper deals with H∞state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H∞state estimation is proposed to estimate the H∞performance and global asymptotic stability of the concerned neural networks. By constructing the Lyapunov–Krasovskii functional and using the linear matrix inequality technique, sufficient conditions for delay-dependent H∞performances are obtained, which can be easily solved by some standard numerical algorithms. Finally, numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results. This paper deals with H∞state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H∞state estimation is estimated to estimate the H∞ performance and global asymptotic stability of the concerned neural networks. By constructing the Lyapunov-Krasovskii functional and using the linear matrix inequality technique, sufficient conditions for delay-dependent H∞ performances are obtained, which can be easily solved by some standard numerical algorithms. Finally, numerical examples are to illustrate the usefulness and effectiveness of the proposed theoretical results.