This　paper　presents　a　progressive　approach　for　assigning　alternatives　that　are　defined　by　multiple　criteria　into　a　set　of　ordered　categories.　It　extends　the　UTADIS　method　to　take　into　account　two　types　of　imprecise　assignment　information　and　allow　the　DM　to　provide　preference　information　in　an　interactive　way.　In　this　approach,　a　heuris-tic　algorithm　is　given　to　build　and　update　the　DM＇s　global　utility　function　and　then　a　mixed　integer　linear　programming　model　is　developed　to　identify　the　inconsistency　of　the　DM＇s　assignment　information　in　the　iterative　process.　When　inconsistency　occurs,　the　conicting　assignment　results　are　presented　to　the　DM　to　help　him　modify　his　previous　information.　However,　when　the　information　is　consistent,　the　fittest　category　and　the　range　of　possible　categories　on　each　alternative　are　obtained　for　the　DM.　The　fittest　cat-egory　is　computed　through　the　previous　mixed　integer　linear　programming　model,　and　the　range　of　possible　categories　is　inferred　by　another　two　mixed　integer　linear　programming　models.　Meanwhile,　the　rationality　of　the　latter　two　mixed　integer　linear　programming　models　is　also　justified　theoretically.　Finally,　an　example　of　MBA　programs　is　given　as　an　illustration　of　the　proposed　approach.　©　2011.