Rapid　and　accurate　identification　of　consumer　demands　and　systematic　assessment　of　product　quality　are　essential　to　success　for　new　product　development,　in　particular　for　fast　moving　consumer　goods　such　as　food　and　drink　products.　This　paper　reports　an　investigation　into　a　belief　rule-based　(BRB)　methodology　for　quality　assessment,　target　setting　and　consumer　preference　prediction　in　retro-fit　design　of　food　and　drink　products.　The　BRB　methodology　can　be　used　to　represent　the　relationships　between　consumer　preferences　and　product　attributes,　which　are　complicated　and　nonlinear.　A　BRB　system　can　initially　be　established　using　expert　knowledge　and　then　optimally　trained　and　validated　using　data　generated　from　consumer　or　expert　panel　assessments　or　from　tests　and　experiments.　The　established　BRBs　can　then　be　used　to　predict　the　consumer　acceptance　of　new　products　or　set　product　target　values　in　retro-fit　design.　The　proposed　BRB　methodology　is　applied　to　the　design　of　a　lemonade　drink　product　using　real　data　provided　by　a　sensory　product　manufacturer　in　the　UK.　The　results　show　that　the　BRB　methodology　can　be　used　to　predict　consumer　preferences　with　high　accuracy　and　to　set　optimal　target　values　for　product　quality　improvement.　(C)　2011　Elsevier　Ltd.　All　rights　reserved.