Because of recent advances in graphics hard- and software, both the production and use of 3D models are increasing at a rapid pace. As a result, a large number of 3D models have become available on the web, and new research is being done on 3D model retrieval methods. Query and retrieval can be done solely based on associated text, as in image retrieval, for example (e.g. Google Image Search  and [2, 3]). Other research focuses on shape-based retrieval, based on methods that measure shape similarity between 3D models (e.g., ). The goal of our work is to take current text- and shape-based matching methods, see which ones perform best, and compare those. We compared four text matching methods and four shape matching methods, by running classification tests using a large database of 3D models downloaded from the web . In addition, we investigated several methods to combine the results of text and shape matching. We found that shape matching outperforms text matching in all our experiments. The main reason is that publishers of online 3D models simply do not provide enough descriptive text of sufficient quality: 3D models generally appear in lists on web pages, annotated only with cryptic filenames or thumbnail images. Combining the results of text and shape matching further improved performance. The results of this paper provide added incentive to continue research in shape-based retrieval methods for 3D models, as well as retrieval based on other attributes.