We investigate a range of solutions in car `make and model' recognition. Several different feature detection approaches are investigated and applied to the problem including a new approach based on Harris corner strengths. This approach recursively partitions the image into quadrants, the feature strengths in these quadrants are then summed and locally normalised in a recursive, hierarchical fashion. Two different classification approaches are investigated; a k-nearest-neighbour classifier and a Naive Bayes classifier. Our system is able to classify vehicles with 96.0% accuracy, tested using leave-one-out cross-validation on a realistic dataset of 262 frontal images of cars.