Cracking　characterization　is　one　of　the　most　important　tasks　in　automated　pavement　data　analysis.　Although　cracking　detection　and　segmentation　algorithms　have　become　more　reliable　in　recent　years,　accurate　cracking　classification　remains　a　constant　challenge　to　pavement　engineers.　Conventionally,　manual　recognition　uses　cracking　orientation　and　topological　features　to　classify　cracking　into　different　types　such　as　alligator　cracking　and　transverse　cracking.　However,　the　rules　to　classify　cracking　are　often　complicated　and　subjective,　which　compromises　the　reliability　of　computerized　implementation.　This　study　develops　a　support　vector　machine　(SVM)-based　method　to　intelligently　identify　cracking　types　in　an　automated　manner.　Pavement　cracks　are　grouped　using　a　minimum　rectangular　cover　(MRC)　model.　Using　the　relative　location,　orientation,　and　size　of　the　MRC,　as　well　as　the　cracking　characteristics　such　as　cracking　density　and　cracking　connectivity,　three　SVM　models　are　compared　in　this　study.　It　is　found　that　an　88.07%　accuracy　is　achieved　for　10,134　MRCs　collected　from　four　highway　sections　using　the　two-phase　SVM　model.　The　proposed　methodological　framework　would　improve　overall　accuracy　in　cracking　classification.　(C)　2017　American　Society　of　Civil　Engineers.