This paper proposes a supervised segmentation method for detecting surface changes based on appearance attributes, focusing on cultural heritage metal surfaces. Reflectance Transformation Imaging (RTI) reconstruction coefficients (PTM and HSH) are explored for tracking changes over time on different data sets. Each acquisition is normalised to ensure the method's robustness, allowing consecutive acquisitions with different RTI acquisition parameters. The proposed method requires expert labelling on groups of pixels representing individual classes. Afterward, the surface appearance is identified over time based on the estimated discriminant model. After segmentation, each detected category is assigned to a single colour to present the results with a user-friendly colourmap visualisation. The method is user-dependent; the labelling of the pixels must be accurately defined based on the research question. The results were evaluated based on human expertise in the conservation-restoration field and are considered ground truth in this work. A case study with visibly segmentable characteristics was used to prove the concept and evaluate the invariance of the proposed method. Comparison with the segmentation of the visible characteristics shows very accurate segmentation for HSH (99%) and lower for PTM (80%), which is influenced by surface rotation. The method was tested on metal surfaces undergoing accelerated corrosion or cleaning treatments. The results were promising for tracking changes based on segmentation. Equally promising is the possibility of qualitative quantifying the degree of change by counting the change of a selected class of pixels. PTM and HSH results are comparable in cases of mat surfaces; however, in high specular surfaces, HSH seems to provide more detailed information and, therefore, can better depict the surface characteristics. Limitations of the application are related to the possibility of identifying surface characteristics that do not exhibit topographic changes or significant reflectance differentiation.