This paper presents a sequential learning algorithm and evaluates its performance by sing it to buildup an RBF network for complex-valued communication channel equalization problems. The algorithm is referred to as Complex Minimal Resource Allocation Network (CMRAN) algorithm and it is an extension of the MRAN algorithm originally developed for online learning in real valued RBF networks. CMRAN has the ability to grow and prune the (complex) RBF network 's hidden neurons to ensure a parsimonious network structure. Simulation results presented clearly show that CMRAN is very effective in equalization problems with performance achieved often being superior to that of some of the well-known methods.