The problem of finite-time $H_{\infty}$ control for uncertain fractional-order neural networks is investigated in this paper. By using finite-time stability theory and the Lyapunovlike function method, we first derive a new condition for problem of finite-time stabilization of the considered fractional-order neural networks via linear matrix inequalities (LMIs). Then a new sufficient stabilization condition is proposed to ensure that the resulting closedloop system is not only finite-time bounded but also satisfies finite-time $H_{\infty}$ performance. Three examples with simulations have been given to demonstrate the validity and correctness of the proposed methods. The problem of finite-time $H_{\infty}$ control for uncertain fractional-order neural networks is investigated in this paper. By using finite-time stability theory and the Lyapunovlike function method, we first derive a new condition for problem of finite-time stabilizationof the considered fractional-order neural networks via linear matrix inequalities (LMIs).Then a new sufficient stabilization condition is proposed to ensure that the resulting closedloop system is not only finite-time bounded but also satisfies finite-time H¥performance.Three examples with simulations have been given to demonstrate the validity and correctness of the proposed methods.