Consumer　preference　prediction　is　a　key　factor　to　the　success　of　new　product　development.　This　paper　presents　a　hybrid　evidential　reasoning　(ER)　and　belief　rule-based　(BRB)　methodology　for　consumer　preference　prediction　and　a　novel　application　to　orange　juices.　The　orange　juices　are　distinguished　by　their　values　of　sensory　attributes,　which　are　grouped　for　simplicity　into　different　categories　such　as　appearance,　aroma,　texture,　flavour,　and　aftertaste.　The　ER　approach　is　used　to　aggregate　consumer　preferences　for　category　attributes　into　an　overall　preference,　and　the　BRB　methodology　is　used　to　model　the　casual　relationships　between　category　attributes　and　their　sensory　attributes.　The　casual　relationships　between　the　overall　preference　and　the　sensory　attributes　of　orange　juices　are　trained　and　tested　using　real　data　and　memorized　for　prediction　or　new　product　design.　A　case　study　involving　16　orange　juices　is　conducted　using　the　proposed　hybrid　ER　and　BRB　methodology　to　demonstrate　its　novel　applications.　The　results　show　that　the　hybrid　ER　and　BRB　methodology　can　fit　and　predict　consumer　preferences　with　high　accuracy.　©　2008　Elsevier　Ltd.　All　rights　reserved.