Extended　object　tracking　and　classification　(EOTC)　using　multisensor　kinematic　and　attribute　data　is　a　challenging　and　highly　coupled　problem.　It　is　a　joint　decision　and　estimation　(JDE)　problem.　A　good　solution　has　to　handle　effectively　the　coupling　between　tracking　and　classification　and　also　make　good　use　of　multisensor　data.　For　this　purpose　and　based　on　the　recently　proposed　JDE　framework,　this　letter　proposes　a　conditional　JDE　(CJDE)　risk,　which　integrates　the　tracking　error　and　the　classification　cost　using　heterogeneous　sensor　data.　Object　classes　differ　from　each　other　in　maneuverability　and　feature　attribute.　A　suitable　model　is　identified　and　used　to　describe　object　classes　differing　in　maneuverability.　Also　presented　are　attribute　evolution　and　measurement　models.　Then,　an　EOTC　algorithm　optimizing　the　CJDE　risk　is　proposed,　which　considers　the　coupling　and　the　information　contained　in　various　data.　Simulation　results　demonstrate　the　superiority　of　the　proposed　EOTC　algorithm　in　joint　performance.