A vision system capable of extracting features from a semi-structured environment for vehicle guidance is described in this paper. The system is primarily used for road following via the detection of mud tracks in a tropical environment. The scene captured by a CCD colour camera is digitised into 24-bit colour images with a resolution of 320x240 pixels. Partitioning of the scene into road and non-road areas is based on the results of a colour image segmentation algorithm applied to these images. The RGB colour images from the camera are converted to HSI format. Training samples of road and non-road features of the terrain to be explored, stored in a database, are used to classify blocks of pixels using only the hue information content of the images. A Bayesian classifier in conjunction with a smooth thresholding function is used for the segmentation algorithm on a per block basis. This approach results in the recognition of traversable areas, particularly non-metalled roads. Experimental results have showed that the algorithm is invariant to shadow conditions, i.e. roads were detected under varying light conditions. Due to the soil conditions of the test sites, small puddles of water on the mud tracks are also classified as driveable areas. The system outputs a one bit 2-D map of the image every 200ms. Field results of the proposed approach have shown favourable responses for real-time implementation on an autonomous ground vehicle.