The　fault-tolerant　control　(FTC)　issue　is　considered　in　this　article　for　Markovian　jump　systems　(MJSs)　in　which　both　nonlinearity　and　actuator　faults　exist　simultaneously.　The　existed　nonlinearity　in　the　considered　MJSs　means　that　there　exist　limitations　to　employ　the　renown　sliding　mode　control　(SMC)　method　directly.　In　this　work,　the　radial　basis　function　(RBF)　neural　network　(NN)　technique　is　exploited　to　model　the　nonlinearity　on　which　no　knowledge　whatsoever　is　available.　Then,　with　the　help　of　the　adaptive　backstepping　method,　an　NN-based　FTC　approach　is　proposed　to　overcome　the　considered　challenging　case.　The　adverse　effects,　arising　from　the　nonlinearity　and　the　actuator　faults　can　be　completely　compensated　by　the　proposed　adaptive　controller.　With　the　proposed　controller　and　the　adaptation　laws,　the　bounded　stability　of　the　considered　closed-loop　plant　can　be　guaranteed.　Furthermore,　only　two　types　of　adaptive　parameters　are　adopted　in　the　proposed　approach　to　achieve　the　purpose　of　FTC,　and　this　reduces　the　computational　burden　and　thus　extends　its　applicability.　Finally,　the　effectiveness　of　the　developed　approach　is　demonstrated　on　a　practical　system:　a　wheeled　mobile　manipulator.