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This paper provides an effective Web content-based image retrieval algorithm by using SIFT (scale invariant feature transform) feature. Different from other existing text-based Web image search engines, this algorithm can be applied to content-based Web image search engine effectively. 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 saved as XML files can be extracted more accurately by using SIFT than by 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 of ROI (Region of interest) and database from Web training images. The experimental results show that this method improves the stability and precision of image retrieval.