Ant　colony　optimization　(ACO)　algorithms　have　been　applied　successfully　to　combinatorial　optimization　problems.　Rough　set　theory　offers　a　viable　approach　for　feature　selection　from　data　sets.　In　this　paper,　the　basic　concepts　of　rough　set　theory　and　ant　colony　optimization　are　introduced,　and　the　role　of　the　basic　constructs　of　rough　set　approach　in　feature　selection,　namely　attribute　reduction　is　studied.　Base　above　research,　a　rough　set　and　ACO　based　algorithm　for　feature　selection　problems　is　proposed.　Finally,　the　presented　algorithm　was　tested　on　UCI　data　sets　and　performed　effectively.