||The vehicle industry is, as society in general, evolving towards increasing machine intelligence. A large step in vehicle intelligence would be for the vehicle to be aware of the scene in which it is located. Scene classification is a growing field in image analysis, and much progress has been made in the last few years. This thesis aims at employing scene classification in real-time to images constructed from an infrared sensor that is mounted at the front of a vehicle. The images are greyscale, and the specific task studied is the two-class task of separating city and rural road scenes. Several image representation methods for scene classification, such as 'Edge Direction Histograms', and 'Invariant Moments' have been evaluated, but most focus has been turned toward the 'Bag of Words' algorithm for image representation. This algorithm has been implemented using both sparse and dense extraction of image elements and using both descriptors based on simple representation of square grey patches and the popular SIFT representation. Using the 'Bag of Words' algorithm, a method for fast two-class scene classification, suitable for a real-time application is proposed. The proposed method uses grey patch based image elements, a modified floating search algorithm for visual word selection and an SVM classifier for the final classification.