A　forest　fire　has　long　been　a　severe　threat　to　the　forest　resources　and　human　life.　The　threat　could　effectively　be　mitigated　by　timely　and　accurate　detection.　In　this　paper,　we　propose　a　novel　accurate　forest　fire　detection　system　using　Wireless　Sensor　Networks　(WSNs).　In　the　proposed　system,　the　detection　accuracy　is　increased　by　applying　the　multi-criteria　detection　that　an　alarm　decision　depends　on　multiple　attributes　of　a　forest　fire.　The　multi-criteria　detection　is　implemented　by　the　artificial　neural　network　which　fuses　sensing　data　corresponding　to　multiple　attributes　of　a　forest　fire　into　an　alarm　decision.　Due　to　the　utilization　of　the　artificial　neural　network,　the　proposed　system　enjoys　low　overhead　and　the　self-learning　capability.　Furthermore,　we　have　developed　a　prototype　consisting　TelosB　sensor　nodes　and　carried　out　extensive　experiments　to　study　the　performance　of　the　proposed　system.　We　have　also　developed　a　solar　battery　in　order　to　persistently　power　the　unattended　sensor　node　deployed　in　the　forest.　©　2011　IEEE.