Many　multiple　attribute　decision　analysis　(MADA)　problems　are　characterised　by　both　quantitative　and　qualitative　attributes　with　various　types　of　uncertainties.　Incompleteness　(or　ignorance)　and　vagueness　(or　fuzziness)　are　among　the　most　common　uncertainties　in　decision　analysis.　The　evidential　reasoning　(ER)　approach　has　been　developed　in　the　1990s　and　in　the　recent　years　to　support　the　solution　of　MADA　problems　with　ignorance,　a　kind　of　probabilistic　uncertainty.　In　this　paper,　the　ER　approach　is　further　developed　to　deal　with　MADA　problems　with　both　probabilistic　and　fuzzy　uncertainties.　In　this　newly　developed　ER　approach,　precise　data,　ignorance　and　fuzziness　are　all　modelled　under　the　unified　framework　of　a　distributed　fuzzy　belief　structure,　leading　to　a　fuzzy　belief　decision　matrix.　A　utility-based　grade　match　method　is　proposed　to　transform　both　numerical　data　and　qualitative　(fuzzy)　assessment　information　of　various　formats　into　the　fuzzy　belief　structure.　A　new　fuzzy　ER　algorithm　is　developed　to　aggregate　multiple　attributes　using　the　information　contained　in　the　fuzzy　belief　matrix,　resulting　in　an　aggregated　fuzzy　distributed　assessment　for　each　alternative.　Different　from　the　existing　ER　algorithm　that　is　of　a　recursive　nature,　the　new　fuzzy　ER　algorithm　provides　an　analytical　means　for　combining　all　attributes　without　iteration,　thus　providing　scope　and　flexibility　for　sensitivity　analysis　and　optimisation.　A　numerical　example　is　provided　to　illustrate　the　detailed　implementation　process　of　the　new　ER　approach　and　its　validity　and　wide　applicability.　(c)　2004　Elsevier　B.V.　All　rights　reserved.