This project explores a method to improve the railway overhead catenary inspection process through automation. The automation is performed by extraction of information from railway overhead catenary through the processing of point cloud data collected using LiDAR instrument. The main focus is on the extraction of wires and measurement of the distance between reinforcement wire and tension wire.Current methods of railway overhead catenary inspection depend on the presence of specialised equipment and personnel on the track. This is costly and time-consuming and above all occupies scheduled time on the track. The process depends on visual inspection which slows down the traffic. Automation in data collection, processing, and detection of anomalies can lead to a reduction in time and cost and will lead to improving overall safety. The main purpose of this research is to contribute to the increase of railway infrastructure capacity, through enablement of the Prognostics and Health Management (PHM), utilising digital technology and Artificial Intelligence (AI). As the first step towards this goal of digitalisation, an approach towards digital representation of railway catenary is being developed.