In　this　paper,　a　self-organizing　intelligent　controller　(SOIC)　is　proposed　for　a　class　of　nonlinear　systems.　The　basic　idea　of　this　study　is　to　use　a　self-organizing　fuzzy　neural　network　to　imitate　control　law　directly,　and　then,　appeal　to　obtain　a　compact　structure　of　controller　to　further　reduce　the　computational　burden　and　enhance　the　control　performance.　First,　an　effective　criterion,　using　the　tracking　performance　and　structure　risk　of　controller,　is　developed　to　self-organize　the　control　rules　online　for　SOIC　to　improve　the　tracking　performance.　Second,　the　structure　and　parameters　of　SOIC　are　updated　by　an　adaptive　projection-type　algorithm　to　reduce　the　heavy　computational　burden　to　speed　up　the　control　response.　Third,　the　stability　of　SOIC　is　proved　in　the　sense　of　Lyapunov　and　the　guidelines　for　selecting　the　control　parameters　are　given.　Finally,　the　effectiveness　of　SOIC　is　illustrated　with　three　nonlinear　systems.　It　is　shown　that　the　proposed　SOIC　can　achieve　better　control　performance　in　comparison　with　some　other　control　schemes.