Crisp　comparison　matrices　lead　to　crisp　weight　vectors　being　generated.　Accordingly,　an　interval　comparison　matrix　should　give　an　interval　weight　estimate.　In　this　paper,　a　goal　programming　(GP)　method　is　proposed　to　obtain　interval　weights　from　an　interval　comparison　matrix,　which　can　be　either　consistent　or　inconsistent.　The　interval　weights　are　assumed　to　be　normalized　and　can　be　derived　from　a　GP　model　at　a　time.　The　proposed　GP　method　is　also　applicable　to　crisp　comparison　matrices.　Comparisons　with　an　interval　regression　analysis　method　are　also　made.　Three　numerical　examples　including　a　multiple　criteria　decision-making　(MCDM)　problem　with　a　hierarchical　structure　are　examined　to　show　the　potential　applications　of　the　proposed　GP　method.　©　2006　Elsevier　B.V.　All　rights　reserved.