This　paper　proposes　a　novel　input-output　clustering　approach　for　structure　identification　of　T-S　fuzzy　neural　networks.　This　approach　consists　of　two　phases.　Firstly,　k-means　clustering　method　is　applied　to　the　input　data　to　provide　the　initial　clusters　of　the　input　space.　Secondly,　check　whether　the　sub-clustering　is　needed　for　each　input　cluster　by　considering　the　corresponding　output　variation　and　then　apply　the　k-means　method　to　further　partition　those　input　clusters　needed　sub-clustering.　Applying　the　above　process　recursively　leads　to　the　structure　identification　of　a　T-S　fuzzy　neural　network　and　then　the　parameter　identification　is　completed　by　using　the　gradient　learning　algorithm.　The　experiments　by　applying　the　proposed　method　to　several　benchmark　problems　show　better　performance　compared　with　many　existing　methods　and　then　verify　the　effectiveness　and　usefulness　of　the　proposed　method.　©　2014　IEEE.