In　joint　tracking　and　classification　(JTC)　problems,　both　decision　and　estimation　are　involved　and　they　affect　each　other.　Good　solutions　for　JTC　require　solving　the　two　problems　jointly.　A　joint　decision　and　estimation　(JDE)　framework　based　on　a　generalized　Bayes　risk　was　recently　proposed　for　solving　the　problem　of　inter-dependent　decision　and　estimation.　In　the　JDE　framework,　a　conditional　JDE　(CJDE)　risk　was　proposed,　and　the　corresponding　optimal　solution　was　obtained.　Due　to　the　development　of　modern　sensor　technology,　multisensor　data　with　different　characteristics　are　available.　In　this　paper,　we　solve　a　JTC　problem　using　multisensor　data　by　the　CJDE　method.　First,　a　dynamic　JTC　problem　based　on　kinematic　and　attribute　measurements　is　formulated　as　a　JDE　problem.　To　solve　this　problem,　we　propose　a　multiple-model　recursive　CJDE　(RCJDE)　method,　which　is　an　extension　of　the　original　RCJDE　to　the　multisensor　scenario.　For　joint　performance　evaluation,　we　suggest　two　joint　performance　metrics　(JPM)　for　the　cases　with　known　and　unknown　ground　truth,　respectively.　Simulation　results　demonstrate　the　effectiveness　of　the　proposed　RCJDE　method.　They　show　that　the　multisensor　data　based　RCJDE　can　outperform　the　traditional　two-step　strategies　in　JPM.