In this paper, an adaptive control algorithm via delayed dynamical neural nets (DDNNs) for a class of nonlinear systems is presented. We identify the nonlinear system by updating the weights of the DDNNs and then design the controller adaptively based on the neural networks model to achieve the model following purpose. An analysis via Lyapunov stability criteria shows that the proposed control algorithm guarantees parameter estimation convergence and system stability, with the output of the system following the specified reference model. Finally, a series of simulations are performed to demonstrate the effectiveness of the proposed scheme.