It　is　proposed　in　this　paper　a　novel　two-stage　structural　damage　detection　approach　using　fuzzy　neural　networks　(FNNs)　and　data　fusion　techniques.　The　method　is　used　for　structural　health　monitoring　and　damage　detection,　particularly　for　cases　where　the　measurement　data　is　enormous　and　with　uncertainties.　In　the　first　stage　of　structural　damage　detection,　structural　modal　parameters　derived　from　structural　vibration　responses　are　fed　into　an　FNN　as　the　input.　The　output　values　from　the　FNN　are　defuzzified　to　produce　a　rough　structural　damage　assessment.　Later,　in　the　second　stage,　the　values　output　from　three　different　FNN　models　are　input　directly　to　the　data　fusion　center　where　fusion　computation　is　performed.　The　final　fusion　decision　is　made　by　filtering　the　result　with　a　threshold　function,　hence　a　refined　structural　damage　assessment　of　superior　reliability.　The　proposed　approach　has　been　applied　to　a　7-degree　of　freedom　building　model　for　structural　damage　detection,　and　proves　to　be　feasible,　efficient　and　satisfactory.　Furthermore,　the　simulation　result　also　shows　that　the　identification　accuracy　can　be　boosted　with　the　proposed　approach　instead　of　FNN　models　alone.　©　2010　Elsevier　Ltd.　All　rights　reserved.