A　very　important　aspect　of　multisensor　data　fusion　is　track-to-track　association　and　track　fusion　in　distributed　multisensor-multitarget　environments.　There　is　a　assumption　for　the　proposed　approach　based　on　Hopfield　neural　network　that　every　sensor　detect　the　same　targets,　but　in　practice,　it　is　not　always　realizable.　This　paper　propose　a　generalized　approach　based　on　continuous　state　Hopfield　neural　network　(CHNN)　to　solve　this　problem.　Furthermore,　the　algorithm　is　generalized　to　system　of　three　sensors.　Also,　the　Mahalanobis　distance　is　redefined　in　this　paper　to　accelerate　the　convergence　of　the　Hopfield　networks.　Computer　simulation　results　indicate　that　this　approach　successfully　solves　the　track-to-track　association　problem,　and　it　can　be　generalized　in　distributed　mutisensor-multitarget　environment.　©2009　IEEE.