Overhead cables, used in railways and power transmission, are termed linear distributed assets due to their spread over large regions. Prognostics and health management of linear distributed assets are challenging due to their spread over a large area, climatic conditions, and dependence on specialised equipment and personnel for inspection. Structural information of overhead cables is required for condition monitoring, anomaly detection, prognostics, and health management. Automation of condition monitoring depends on the acquisition and processing of structural information. This paper presents an algorithm for the extraction of individual cables. A step towards health assessment of railway catenary cables, focusing on extracting individual cables. Structural information is extracted through point cloud data processing. This paper presents a novel approach for clustering of point-cloud data with known features. The proposed approach is based on an adaptation of the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, termed as “Feature Aligned DBSCAN”, which improves the candidate evaluation capability based on point cloud features during the cluster expansion stage. Finally, the paper verifies the inter-cable distance measurement algorithm from laboratory data. The methodology presented in this paper enables the railway catenary health assessment process to be spread over large regions, reducing the time and cost requirements while improving overall safety.