The　gold　immunochromatographic　assay　has　the　advantages　of　simple　operation,　low　costs　and　rapid　operation　time.　But　the　traditional　immunochromatographic　strip　can　only　get　qualitative　or　semi-quantitative　results　observed　directly　with　the　naked　eyes,　the　disadvantages　are　low　measurement　accuracy,　and　it　is　difficult　to　achieve　quantitative　measurement.　This　paper　presents　a　new　method　to　perform　the　unsupervised　classification　for　the　gold　immunochromatographic　strip　by　combining　the　histogram　features　vectors　and　the　fuzzy　C-means　algorithm　based　on　computer　image　analysis　system.　Then　provide　procedures　of　extracting　the　histogram　features　as　input　vectors　for　fuzzy　C-means　algorithm　and　example　of　the　fuzzy　C-means　clustering　analysis　methods　of　the　gold　immunochromatographic　strip.　In　the　experiment,　the　discrimination　coefficient　Classification　coefficient　F　is　0.8953　and　the　Average　fuzzy　entropy　H=0.085,　the　gold　immunochromatographic　strips　with　various　human　chorionic　gonadotropin　(hCG)　concentrations　were　accurate　and　unsupervised　classified　to　three　clustering.　The　result　proves　that　the　classification　of　the　gold　immunochromatographic　strips　by　the　histogram　features　vectors　and　the　fuzzy　C-means　algorithm　is　reasonable　and　validated,　it　offers　a　good　semiquantitative　and　quantitative　test　method　to　the　immunochromatographic　strip　for　clinical　diagnosis.　The　research　can　not　only　enhance　the　detection　sensitivity　and　the　objectivity　of　test　result,　but　also　have　a　sound　application　value.　©　2011　IEEE.