This　article　investigates　the　problem　of　fast　finite-time　adaptive　neural　fault-tolerant　tracking　control　for　multi-input　multi-output　(MIMO)　nonlinear　systems　with　full-state　constraints　and　actuator　faults.　The　radial　basis　function　neural　networks　are　introduced　to　deal　with　unknown　nonlinear　functions.　In　addition,　an　additive　transformation　and　one-to-one　mapping　method　are　employed　to　deal　with　the　control　problem　of　MIMO　nonlinear　systems　with　full-state　constraints.　Based　on　the　fast　finite-time　stability　theory　and　adaptive　backstepping　technique,　which　guarantees　all　the　closed-loop　system　signals　are　bounded,　the　tracking　error　eventually　converges　to　a　small　neighborhood　of　the　origin　in　a　fast　finite-time　and　full-state　constraints　are　not　violated.　Finally,　simulation　results　demonstrate　the　feasibility　of　the　proposed　control　scheme.