Osteoarthritis (OA) is one of the most common degenerative wear diseases of knee joints worldwide. Within knee joint articular cartilage (AC), collagen structure losing integrity is a major symptom of OA. Based on the development of laser scanning confocal microscopy (LSCM), AC images containing three-dimensional (3D) surface texture information could be obtained for quantitative analysis using numerical parameters. Numerical analysis results could be applied for OA diagnosis and progression assessment. Unique to existing numerical analysis techniques, two surface texture parameter sets named field and feature parameters have been used to study wear features in this study. Field parameters are statistically applied to consecutive surface of each scale-limited surface portion while feature parameters are statistically used for a sub-set of pre-defined topographic features. In this study, the feature parameters are for the first time innovatively used for numerical analysis of wear testing AC samples. This project has also selected critical parameters from the field and feature parameter sets to study the correlation between the AC surface changes and OA development. The results presented in the paper have demonstrated that the selected field parameters describe the wear trait of the surfaces for the study of OA progression and the selected feature parameters characterise surface features and their relationship for the study of the functional performance of the surfaces. By utilizing the proposed analysis approach, it is possible to enhance the current numerical analysis techniques for both OA status monitoring and problem diagnosis.