Because a single feature cannot represent all of the information contained in an inscription image, a method for evaluating bronze inscription images using multi-measurement similarity is proposed in this study. This method is based on the global Hu moment as well as local term frequency-inverse document frequency (TF-IDF) and K-means speeded-up robust features (SURF), referred to as cluster weighted SURF (TF-KSURF). By extracting the Hu moment feature descriptor and the SURF matrix, the global and local features of the inscription image are obtained simultaneously. In addition, the K-means algorithm and weighting strategy are used to cluster and weight the local SURF to construct the TF-KSURF vector. The weights of the two measures are set to form a multi-measure similarity function, which is applied to image retrieval of bronze inscriptions. The experimental results show that compared with the single feature measure, the proposed multi-measure similarity method can be used to accurately analyze the overall characteristics of the inscriptions and to improve the retrieval performance.