Tongue　diagnosis　characterization　is　a　key　research　issue　in　the　development　of　Traditional　Chinese　Medicine　(TCM).　Many　kinds　of　information,　such　as　tongue　body　color,　coat　color　and　coat　thickness,　can　be　reflected　from　a　tongue　image.　That　is,　tongue　images　are　multi-label　data.　However,　traditional　supervised　learning　is　used　to　model　single-label　data.　In　this　paper,　multi-label　learning　is　applied　to　the　tongue　image　classification.　Color　features　and　texture　features　are　extracted　after　separation　of　tongue　coat　and　body,　and　multi-label　learning　algorithms　are　used　for　classification.　Results　showed　LEAD　(Multi-Label　Learning　by　Exploiting　Label　Dependency),　a　multi-label　learning　algorithm　demonstrating　to　exploit　correlations　among　labels,　is　superior　to　the　other　multi-label　algorithms.　At　last,　the　iteration　algorithm　is　used　to　set　an　optimal　threshold　for　each　label　to　improve　the　results　of　LEAD.　In　this　paper,　we　have　provided　an　effective　way　for　computer　aided　TCM　diagnosis.