One of the fundamental issues in the operation of a mobile communication system is the assignment of channels to cells and to calls. Since the number of channels allocated to a mobile communication system is limited, efficient utilization of these communication channels by using efficient channel assignment strategies is not only desirable but also imperative. This paper presents a novel approach to solving the dynamic channel assignment (DCA) problem by using a form of realtime reinforcement learning known as Q-learning in conjunction with neural network representation. Instead of relying on a known teacher, the system is designed to learn an optimal channel assignment policy by directly interacting with the mobile communication environment. The performance of the Q-learning-based DCA was examined by extensive simulation studies on a 49-cell mobile communication system under various conditions. Comparative studies with the fixed channel assignment (FCA) scheme and one of the best dynamic channel assignment strategies, MAXAVAIL, have revealed that the proposed approach is able to perform better than the FCA in various situations and capable of achieving a performance similar to that achieved by the MAXIAVIAL, but with a significantly reduced computational complexity.