This　paper　provides　a　web　content-based　image　searching　engine　based　on　SIFT　(Scale　Invariant　Feature　Transform)　feature　matching.　SIFT　descriptors,　which　are　invariant　to　image　scaling　and　transformation　and　rotation,　and　partially　invariant　to　illumination　changes　and　affine,　present　the　local　features　of　an　image.　Therefore,　feature　keypoints　can　be　extracted　more　accurately　by　using　SIFT　than　color,　texture,　shape　and　spatial　relations　feature.　To　decrease　unavailable　features　matching,　a　dynamic　probability　function　replaces　the　original　fixed　value　to　determine　the　similarity　distance　and　database　from　training　images.　Then,　by　using　pretreatment　of　the　source　images,　the　keypoints　will　be　stored　to　the　XML　format,　which　can　improve　the　searching　performance.　Finally,　the　results　displayed　to　the　user　through　the　HTML.　The　experimental　results　show　that　this　method　improves　the　stability　and　precision　of　image　searching　engine.　©2009　IEEE.