Accurate and efficient identification of road adhesion coefficient provides important input parameters for active safety system. In this paper, an identification method of road friction coefficient based on Elman neural network was proposed. Through Carsim/Simulink co-simulation, 63 driving conditions and 20 important dynamics responses of a vehicle were obtained. The identification model of road friction coefficient based on Elman neural network was constructed. The road surface with friction coefficient from 0.2 to 0.9 was identified. The average absolute percentage error was 4.92% and the accuracy was 91.22%. Compared with traditional BP neural network method, this method reduced the average absolute percentage error of road friction coefficient by 2.24% and improved the accuracy by 9.82%. Vehicle experiments on wet and dry asphalt pavement verified the effectiveness and feasibility of the proposed method.