This paper presents the design, development and testing of an application to recognize regulatory traffic signs vertically installed on Colombian roads. The application is conceived as a module of a driver assistance system under development, and an autonomous vehicle adapted to the local infrastructure. The application uses Support Vector Machines which are trained and tested with official synthetic images provided by the National Ministry of Transport. These images are modified with chromatic and geometric changes to emulate fluctuations in illumination, vantage point, and ageing. Resulting images are resized to 48 × 48 pixels, and the raw intensity planes in the Hue-Saturation-Intensity color model are reshaped to obtain feature vectors with 2304 attributes each. In total, forty seven binary classifiers were trained under a one-versus-all classification scheme. These classifiers were directly combined into a multi-class classification system. This paper reports the methodology used to collect the data, configure, train, and evaluate the performance of classifiers working isolated and collectively.