In this paper, a consensus maneuvering problem is investigated for uncertain nonlinear systems in strict-feedback form. Consensus maneuvering controllers are developed based on a modular design approach. Specifically, an estimation module is proposed, where a neural network is employed for approximating the unknown nonlinearities. Then, a controller module is designed based on a modified dynamic surface control method. Finally, the input-to-state stability of the close-loop system is analyzed via cascade theory, and the consensus maneuvering error is proved to converge to a residual set.