This thesis aims to create a system using colour markings for Volvo’s autonomous wheel loaders which determines their restricted area and operation using sensors available on the machine. The wheel loader shall be able to interpret and distinguish different colours of spray paint, and depending on the colour, act accordingly. Six different colours are evaluated across two different colour types to find the most suitable ones for the system. Multiple tests are presented throughout the thesis to find the approach with the most optimal performance that meets the system's requirements. The system is evaluated in various weather conditions to determine how the weather affects the performance of the system. The thesis also compares two different line-following approaches, where one is based on edge detection using Canny Edge and Hough transform, and the other uses histogram analysis and sliding window search, to distinguish and track the colour markings. While the wheel loader is in operation, it collects GPS coordinates to create a map of the path taken by the wheel loader and the location of various tasks. The evaluation shows that red, green and blue in fluorescent colour type are the most suitable colours for such a system. The line-following algorithm that utilises perspective warp, histogram and a sliding window search was the most prominent for accurate line detection and tracking. Furthermore, the evaluation showed that the performance of the system was affected depending on the weather condition.