An adaptive sliding mode control scheme using radial basis function network (RBFN) for container cranes is investigated in this study. Here, a sliding surface is designed in such a way that sway motion of the payload is incorporated into the trolley dynamics. In addition, to relax the requirement of mathematical model in the design of a traditional sliding mode control (SMC) system, a neural network compensator, obtained by a radial basis function network and an adaption law, which approximates the nonlinear functions in the traditional SMC control law. This control scheme guarantees the asymptotic stability of the closed-loop system based on Lyapunov theory. To illustrate the efficiency of proposed control strategy, simulation results are provided.