This　paper　addresses　a　novel　hybrid　data-fusion　system　for　damage　detection　by　integrating　the　data　fusion　technique,　probabilistic　neural　network　(PNN)　models　and　measured　modal　data.　The　hybrid　system　proposed　consists　of　three　models,　i.e.　a　feature-level　fusion　model,　a　decision-level　fusion　model　and　a　single　PNN　classifier　model　without　data　fusion.　Underlying　this　system　is　the　idea　that　we　can　choose　any　of　these　models　for　damage　detection　under　different　circumstances,　i.e.　the　feature-level　model　is　preferable　to　other　models　when　enormous　data　are　made　available　through　multi-sensors,　whereas　the　confidence　level　for　each　of　multi-sensors　must　be　determined　(as　a　prerequisite)　before　the　adoption　of　the　decision-level　model,　and　lastly,　the　single　model　is　applicable　only　when　data　collected　is　somehow　limited　as　in　the　cases　when　few　sensors　have　been　installed　or　are　known　to　be　functioning　properly.　The　hybrid　system　is　suitable　for　damage　detection　and　identification　of　a　complex　structure,　especially　when　a　huge　volume　of　measured　data,　often　with　uncertainties,　are　involved,　such　as　the　data　available　from　a　large-scale　structural　health　monitoring　system.　The　numerical　simulations　conducted　by　applying　the　proposed　system　to　detect　both　single-　and　multi-damage　patterns　of　a　7-storey　steel　frame　show　that　the　hybrid　data-fusion　system　cannot　only　reliably　identify　damage　with　different　noise　levels,　but　also　have　excellent　anti-noise　capability　and　robustness.　©　2011　Elsevier　Ltd.　All　rights　reserved.