Abstract: For the problem of congestion control in TCP networks, an adaptive sliding mode control algorithm is presented based on the RBF neural network. To simplify the design of the sliding mode controller, the uncertain parameters of the systems and the nonlinear compensation of the systems are incorporated into a lumped uncertainty. Since the upper bound of the system uncertainties may not easily be obtained, a RBF neural network is used to learn the upper bound of system uncertainties. And the output of the RBF neural network is used to compensate the upper bound of system uncertainties, so that the effects of the system uncertainties can be eliminated. The RBF neural network is used to design an adaptive sliding mode controller which not only ensure the existence of the sliding mode on the surface and asymptotic stability of the systems, but also eliminate the effects of the system uncertainties. Simulation results verify the favorable stability and robustness of the algorithm.