Handling　both　intermittent　actuator　faults　and　coupled　interconnections　in　uncertain　multiple-input–multiple-output　nonlinear　system　is　still　a　challenge　in　the　control　community.　In　this　paper,　to　address　this　issue,　an　adaptive　neural　fault-tolerant　control　scheme　is　developed.　Firstly,　neural　networks　with　random　hidden　nodes　are　used　to　approximate　unknown　functions,　and　an　inequality　is　introduced　to　construct　controllers　such　that　the　singularity　problem　of　the　controllers　can　be　circumvented.　Secondly,　a　projection　algorithm　is　adopted　to　update　online　the　estimated　parameters　in　the　controllers　such　that　the　boundedness　of　estimated　parameters　is　ensured.　In　particular,　the　boundedness　of　estimate　of　unknown　fault　parameters　with　intermittent　jumps　can　be　definitely　guaranteed.　Due　to　the　effects　of　intermittency　jumps　of　unknown　parameters　on　the　system　stability　during　operation,　a　modified　Lyapunov　function　is　developed　to　prove　the　system　stability.　It　is　proved　that　the　system　stability　only　depends　on　the　jumping　amplitude　of　Lyapunov　function　and　the　minimum　fault　time　interval　and　is　not　affected　by　the　total　number　of　faults.　Thirdly,　a　root　mean　square　type　of　bound　for　the　tracking　error　is　established　by　using　iterative　calculation　to　illustrate　that　the　system　transient　performance　in　the　sense　of　the　tracking　error　is　adjustable　by　proper　choice　of　design　parameters.　Finally,　simulation　studies　are　carried　out　to　verify　the　effectiveness　of　the　theoretical　results.　©　2020,　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature.