Pavement　condition　data　usually　consist　of　multiple　performance　indicators,　which　poses　difficulties　in　maintenance　and　fund　allocation　decision　making.　In　conventional　practice,　empirical-based　scoring　methods　have　been　used　to　assess　multidimensional　pavement　condition　attributes.　However,　if　performance　data　on　a　roadway　network　are　available,　data-driven　approaches　can　be　applied　for　such　multiattribute　decision-making　problems.　In　this　study,　an　unsupervised　cluster　method　called　normalized　cuts　(NCut)　is　developed　to　group　pavement　sections　into　clusters　with　homogenous　conditions.　Geometric　centers　of　the　clusters　are　used　to　determine　the　performance　ranking　of　each　cluster.　The　proposed　methodology　is　demonstrated　with　a　case　study　in　Louisiana.　A　total　of　35　pavement　sections　with　eight　performance　parameters　are　grouped　into　five　clusters　indicating　conditions　ranging　from　very　good　to　very　poor.　It　is　validated　with　current　practice　that　the　methodology　presented　in　this　study　is　effective　in　supporting　pavement　prioritization　decision　making.　(C)　2018　American　Society　of　Civil　Engineers.