Modular　neural　network　(MNN)　has　distinct　advantage　in　many　fields　such　as　pattern　recognition　and　pattern　recognition.　However　it　is　still　a　challenge　to　dynamically　adjust　the　MNN　structure　for　dynamic　nonlinear　system　modeling.　This　paper　proposes　a　novel　online　self-organizing　MNN　(OSOMNN)　for　nonlinear　system　modeling.　In　OSOMNN,　an　online　task　decomposition　algorithm　and　a　self-organizing　algorithm　for　subnetwork　are　introduced.　Firstly,　the　task　decomposition　algorithm　is　implemented　by　the　online　clustering　method　based　on　distance　and　local　density,　which　can　online　divide　the　original　task　into　several　simpler　subtasks.　Then　subnetworks　with　single-layer　feedforward　neural　network　are　built　to　learn　the　divided　subtasks.　Moreover,　this　paper　develops　a　self-organizing　algorithm　for　subnetwork,　which　can　dynamically　adjust　its　structure　and　is　trained　by　the　improved　online　gradient　method　with　fixed　memory　mechanism　(FMOGM).　To　demonstrate　the　effectiveness　of　OSOMNN　for　nonlinear　system　modeling,　experimental　investigations　using　four　benchmark　nonlinear　systems　and　the　monthly　sunspots　time　series　show　that　OSOMNN　can　automatically　add　or　merge　the　subnetwork　modules　and　optimize　the　structure　of　subnetworks　for　nonlinear　system　modeling　with　a　better　generalization　performance　than　the　established　alternatives.　©　2020　Elsevier　B.V.