The　DEAHP　method　for　weight　deviation　and　aggregation　in　the　analytic　hierarchy　process　(AHP)　has　been　found　flawed　and　sometimes　produces　counterintuitive　priority　vectors　for　inconsistent　pairwise　comparison　matrices,　which　makes　its　application　very　restrictive.　This　paper　proposes　a　new　data　envelopment　analysis　(DEA)　method　for　priority　determination　in　the　AHP　and　extends　it　to　the　group　AHP　situation.　In　this　new　DEA　methodology,　two　specially　constructed　DEA　models　that　differ　from　the　DEAHP　model　are　used　to　derive　the　best　local　priorities　from　a　pairwise　comparison　matrix　or　a　group　of　pairwise　comparison　matrices　no　matter　whether　they　are　perfectly　consistent　or　inconsistent.　The　new　DEA　method　produces　true　weights　for　perfectly　consistent　pairwise　comparison　matrices　and　the　best　local　priorities　that　are　logical　and　consistent　with　decision　makers　(DMs)＇　subjective　judgments　for　inconsistent　pairwise　comparison　matrices.　In　hierarchical　structures,　the　new　DEA　method　utilizes　the　simple　additive　weighting　(SAW)　method　for　aggregation　of　the　best　local　priorities　without　the　need　of　normalization.　Numerical　examples　are　examined　throughout　the　paper　to　show　the　advantages　of　the　new　DEA　methodology　and　its　potential　applications　in　both　the　AHP　and　group　decision　making.　(c)　2008　Elsevier　B.V.　All　rights　reserved.