The　problem　of　adaptive　neural　fixed-time　tracking　control　for　high-order　systems　is　addressed　in　this　article.　In　order　to　handle　the　difficulties　from　the　uncertain　nonlinearities　within　the　original　systems,　the　radial　basis　function　neural　networks　(RBF　NNs)　are　introduced　to　approximate　the　unknown　nonlinear　functions,　and　the　adding　a　power　integrator　is　applied　to　overcome　the　obstacle　from　high-order　terms.　It　is　proven　that　all　signals　in　the　closed-loop　system　are　bounded　and　the　output　signal　can　eventually　converge　to　a　small　neighborhood　of　the　reference　signal.　Simulation　results　further　verify　the　approaches　developed.