Multiconlitron　is　a　general　geometric　method　for　constructing　piecewise　linear　classifiers,　but　it　was　initially　designed　only　for　two-class　problem.　In　this　paper,　we　propose　a　multi-class　learning　method　of　multiconlitron　by　using　a　hybrid　binary　tree　architecture.　At　each　internal　node　that　does　not　generate　leaf　nodes,　a　hyperplane　is　first　created　as　perpendicular　bisectors　of　line　segment　linking　centroids　of　the　two　farthest　classes　from　each　other.　Then,　according　to　the　positive　or　negative　sides　on　the　hyperplane,　all　the　inherited　classes　are　divided　into　two　groups　for　the　next　iteration.　For　an　internal　node　that　will　generate　leaf　nodes,　the　multiconlitron　is　constructed　by　support　multiconlitron　algorithm,　which　can　separate　one　class　from　the　other　class　(or　group).　Generally,　the　approximate　hyperplane　by　centroids　can　provide　fast　division　in　the　early　stages　of　the　training　phase,　whereas　the　ensemble　boundaries　with　multiconlitron　will　perform　the　final　precise　decision.　As　a　result,　a　hybrid　binary　partition　tree　is　created　which　represents　a　hierarchical　division　of　given　classes.　Experimental　results　show　that　the　proposed　method　is　better　than　one-versus-one　multiconlitron　and　directed　acyclic　graph　multiconlitron,　both　in　terms　of　classification　effectiveness　and　computational　time.　Moreover,　comparison　with　another　tree-based　multi-class　piecewise　linear　classifier　verifies　its　competitiveness　and　superiority.　(C)　2017　Elsevier　B.V.　All　rights　reserved.