It is both challenging and desirable to be able to retrieve sound files relevant to users’ interests by searching the Internet. Unlike the traditional way of using keywords as input to search for web pages with relevant texts, query example can be used as input to search for similar sound files. In this paper, content-based technology has been applied to automatically retrieve sounds similar to the query-example. Features from time, frequency and coefficients domains are firstly extracted from each sound file. Next, Euclidean distances between the vectors of query and sample audios are measured. An ascending distance list is given as retrieval results. Experiments have been conducted on a sound database with 414 files from 16 classes. Further, we propose to classify the query audio into three classes, speech, music and other sound, with much fewer features and then search relevant files only in that subspace. This way, the retrieval performance could be further increased with the saving of computing time as well. Simulations show that our method leads to better results compared to the Soundfisher software in terms of both retrieval quality and completeness.