This　paper　presents　a　method　to　characterize　asphalt　pavement　macrotexture　using　the　gray　level　co-occurrence　matrix　(GLCM).　Data　collected　at　37　field　sites　are　included　in　the　analysis,　representing　6　types　of　asphalt　surface　layers　such　as　dense　asphalt　concrete　(DAC),stone　matrix　asphalt　(SMA),　rubber　asphalt　concrete　(RAC),　ultra-thin　wearing　course　(UTWC),　micro-surfacing　(MS),　and　open　graded　friction　course　(OGFC).　This　paper　documents　the　investigation　into　the　differences　of　GLCM　indicators　under　various　pixel　pair　spatial　relationships.　Then,　the　average　of　each　GLCM　indicator　in　some　pixel　pair　spatial　relationships　is　selected　for　mean　texture　depth　(MTD)　correlation.　The　correlation　analysis　shows　there　are　2　GLCM　indicators,　f8　and　f9,　have　strong　relationship　with　MTD,　which　are　entropy　of　the　gray　level　sum　distribution　and　the　gray　level　combination　distribution　of　pixel　pairs　of　pavement　macrotexture　respectively.　The　larger　the　values　of　f8　and　f9,　the　more　complex　of　the　pavement　macrotexture.　The　correlation　coefficients　between　MTD　and　f8,　f9　are　0.9601　and　0.9493　respectively.　The　exponential　models　are　better　choice　for　connecting　f8and　f9with　MTD,　which　are　highly　significant.　The　mean　square　errors　(MSE)　of　the　exponential　models　with　f8　and　f9　are　0.00343　and　0.00351　respectively.　©　Chinese　Society　of　Pavement　Engineering.