The design of a new car is guided by a set of directives indicating the target market, specific engineering, and aesthetic constraints, which may also include the preservation of the company brand identity or the restyling of products already on the market. When creating a new product, designers usually evaluate other existing products to find sources of inspiration or to possibly reuse successful solutions. In the perspective of an optimized styling workflow, great benefit could be derived from the possibility of easily retrieving the related documentation and existing digital models both from internal and external repositories. In fact, the rapid growth of resources on the Web and the widespread adoption of computer-assisted design tools have made available huge amounts of data, the utilization of which could be improved by using more selective retrieval methods. In particular, the retrieval of aesthetic elements may help designers to create digital models conforming to specific styling properties more efficiently. The aim of our research is the definition of a framework that supports (semi)automatic extraction of semantic data from three-dimensional models and other multimedia data to allow car designers to reuse knowledge and design solutions within the styling department. The first objective is then to capture and structure the explicit and implicit elements contributing to the definition of car aesthetics, which can be realistically tackled through computational models and methods. The second step is the definition of a system architecture that is able to transfer such semantic evaluation through the automatic annotation of car models.