Two-channel quadrature mirror filter banks can be efficiently established by the combination of real-valued infinite impulse response all-pass filters without incurring aliasing and magnitude distortions. The design problem of filter banks is therefore to seek for the phase error minimization of the all-pass filter coefficients. In this paper, a neural network-based Lyapunov energy function is used to relate to the objective function of the designed all-pass filter coefficients. Based on the architecture of neural networks and suitable selection of Hopfield-related parameters, the all-pass filter coefficients are obtained when the networks reach convergence. By further using the suitable combination of the designed all-pass filters, the 2-channel quadrature mirror filter banks with perfect reconstruction can be efficiently accomplished. Simulation results indicate that the neural network-based approach has the advantage of satisfactory performance in magnitude and group delay responses in a parallelism manner.KEYWORDS all-pass filters, infinite impulse response, Lyapunov energy function, neural networks, quadrature mirror filter