The　problem　of　direct　adaptive　neural　control　for　a　class　of　nonlinear　systems　with　an　unknown　gain　sign　and　nonlinear　uncertainty　is　discussed　in　this　paper.　Based　on　the　principle　of　sliding　mode　control　and　the　approximation　capability　of　multilayer　neural　networks　(MNNs),　and　using　Nussbaum-type　function,　a　novel　design　scheme　of　direct　adaptive　neural　control　is　proposed.　By　adopting　the　adaptive　compensation　term　of　the　upper　bound　function　of　the　sum　of　residual　and　approximation　error,　the　closed-loop　control　system　is　shown　to　be　globally　stable,　with　tracking　error　converging　to　zero.　Simulation　results　show　the　effectiveness　of　the　proposed　approach.