We propose an efficient neural network called a dynamic multidimensional wavelet neural network (DMWNN). Since the resulting network based on wavelet theory can provide the efficient representation of a nonlinear function and has the capability to keep some previous information for later use, it can perform effective dynamic mapping with lower input signal dimensions. These features of the DMWNN show one way to compensate for the weakness of the diagonal recurrent neural network (DRNN) and feed-forward wavelet neural network (FWNN). Effectiveness in application of the proposed neural network is also demonstrated through simulation results.