Crisp　comparison　matrices　produce　crisp　weight　estimates.　It　is　logical　for　an　interval　or　fuzzy　comparison　matrix　to　give　an　interval　or　fuzzy　weight　estimate.　In　this　paper,　an　eigenvector　method　(EM)　is　proposed　to　generate　interval　or　fuzzy　weight　estimate　from　an　interval　or　fuzzy　comparison　matrix,　which　differs　from　Csutora　and　Buckley＇s　Lambda-Max　method　in　several　aspects.　First,　the　proposed　EM　produces　a　normalized　interval　or　fuzzy　eigenvector　weight　estimate　through　the　solution　of　a　linear　programming　model,　while　the　Lambda-Max　method　gives　a　series　of　nonnormalized　interval　eigenvector　weight　estimate　through　an　iterative　solution　process.　Next,　the　EM　directly　solves　the　principal　right　eigenvector　of　an　interval　or　fuzzy　comparison　matrix,　while　the　Lambda-Max　method　needs　transforming　a　fuzzy　comparison　matrix　into　a　series　of　interval　comparison　matrices　using　a-level　sets　and　the　extension　principle　and　therefore　needs　the　solution　of　a　series　of　eigenvalue　problems.　Finally,　the　Lambda-Max　method　needs　the　help　of　the　principal　right　eigenvector　of　a　crisp　comparison　matrix　to　determine　the　final　interval　weights,　while　the　EM　has　no　such　requirement.　It　is　also　found　that　not　all　interval　or　fuzzy　comparison　matrices　can　generate　normalized　interval　or　fuzzy　eigenvector　weights.　Situations　where　the　EM　is　inapplicable　are　analyzed　and　the　aggregation　of　local　interval　or　fuzzy　weights　into　global　interval　or　fuzzy　weights　is　also　discussed.　Three　numerical　examples　including　a　hierarchical　structure　decision-making　problem　are　examined　using　the　EM　to　demonstrate　its　applications.　(c)　2006　Elsevier　Inc.　All　rights　reserved.