Current techniques for road-traffic monitoring rely on sensors which have limited capabilities, are inflexible and often, both costly and disruptive to install. The use of video cameras (many of which are already installed to survey road net works), coupled with computer vision techniques offers an attractive alternative to current sensors. Vision based sensors have the potential to measure a far greater variety of traffic parameters compared to conventional sensors. This thesis presents two vision based traffic-monitoring systems. The first is a number-plate recognition system. This is capable of monitoring the output from a video camera and detecting when a vehicle passes by. At this moment an image is captured and the vehicle's number-plate is located and deciphered. The second system is a generic road-traffic monitoring sensor which utilises model based techniques to track vehicles as they manoeuvre through complex road scenes. The position of the vehicle in the image is transformed to the vehicle's position in the real world enabling, among other things, vehicle speed and path to be easily measured. The development of each system is described in detail and results from testing the systems on images from real traffic scenes are presented.