In　this　paper,　a　novel　adaptive　neural　network　(NN)　controller　is　proposed　for　trajectory　tracking　of　autonomous　underwater　vehicle　(AUV).　By　employing　radial　basic　function　neural　network　to　account　for　modeling　errors,　then　the　adaptive　NN　tracking　controller　is　constructed　by　combining　the　dynamic　surface　control　(DSC)　and　the　minimal　learning　parameter　(MLP).　The　proposed　controller　guarantees　that　all　the　close-loop　signals　are　uniform　ultimate　bounded　(UUB)　and　that　the　tracking　errors　converge　to　a　small　neighborhood　of　the　desired　trajectory.　Finally,　simulation　studies　are　given　to　illustrate　the　effectiveness　of　the　proposed　algorithm.　©　2013　Elsevier　B.V.