Based on the extreme learning machine(ELM), a robust adaptive neural control method for a class of multipleinput-multiple-output(MIMO) affine nonlinear dynamic systems is presented. ELM for single-hidden layer feedforward networks(SLFNs), which randomly chooses hidden node parameters and analytically determines the output weights of SLFNs, shows good generalized performance at extremely fast learning speed. The proposed control scheme utilizes the ELM to approximate the plant’s unknown nonlinear terms. Meanwhile, output weights of ELM, unknown upper bound values of approximation errors and external disturbances can be online estimated through parameter adaptive laws by using Lyapunov stability analysis, so that semi-global uniform ultimare boundedness of all signals in the closed-loop system can be guaranteed. Finally, simulation results show the effectiveness of the proposed adaptive ELM control.