In　recent　years,　the　demand　for　reliable　and　accurate　nondestructive　evaluation　(NDE)　of　structural　systems　has　been　growing　and　several　nondestructive　testing　(NDT)　techniques　are　being　combined　to　address　the　issues　of　uncertainty　in　the　testing　results.　However,　the　synergistic　combination　of　multiple　NDT　techniques　and　the　interpretation　of　the　testing　results　require　the　ability　to　process　and　analyze　data　from　several　sources,　a　process　that　is　referred　to　as　＂data　fusion.＂　The　latter　has　received　considerable　attention　since　the　beginning　of　the　21st　century,　thanks　to　the　advances　in　sensor　technologies　and　data　acquisition　systems.　Also,　the　new　era　of　＂Big　Data＂　where　heterogeneous　data　are　measured　by　different　sensors　and　processed　for　a　unified　purpose　is　another　factor　contributing　to　this　rapid　development　and　the　establishment　of　new　NDT　opportunities.　This　article　reviews　the　recent　studies　involving　NDE　data　fusion　for　the　inspection　of　structures　and　examines　the　state-of-the-art　mathematical　expressions　used　in　different　fusion　algorithms.　Challenges　in　the　handling　of　the　NDE　data　and　the　application　of　fusion　algorithms　are　also　identified　and　discussed.　A　generic　framework　of　applying　NDE　data　fusion　is　described,　and　a　roadmap　for　potential　research　prospects　in　NDE　data　fusion　is　provided.