Abstract: In this study, a method for dynamic modeling of a five-link seven degree of freedom (DOF) biped robot has been developed. The method, which is based on neural networks, considerably reduces the complexities in solving the dynamic model equations of the biped robot. Seven neural networks have been synthesized in order for modeling of the seven DOF of the robot being the coordinates of the torso center of mass, the torso angle and the left and right knee and thigh angles. In order for generating data for neural network training, the robot dynamics while walking on a non-smooth two-dimensional surface has been has been considered. The input sets of the trained neural networks consist of four applied torques in addition to the last sample time value of each output. These fed-back outputs not only account for the system second order dynamics but also help on compensating the surface non-smoothness very well. Evaluation results are representative of high performance and much lower complexity of the trained networks with respect to the nonlinear second order robot dynamics model.