A Q-learning-based algorithm is presented for heterogeneous wireless network selection. The proposed algorithm can select the appropriate network for access according to different traffic types, terminal mobility and network load status. Simulation results show that the proposed algorithm has an efficient learning ability to achieve autonomous radio resource management, which effectively improves the spectrum utility and reduces the blocking probability.