This paper describes the development and application of a novel supervised segmentation technique used for conservation documentation based on visible appearance changes of Cultural Heritage (CH) metal surfaces. The technique is based on employing a linear discriminant analysis model to classify Reflectance Transformation Imaging (RTI) reconstruction coefficients. The Hemispherical Harmonics (HSH) reconstruction coefficients for each pixel are first calculated and then normalized. This normalization increases the robustness and invariance of the application making it possible to apply it for documenting different surfaces and at different time intervals. In this paper, we presented three case studies related to corrosion assessment of CH objects through detection of corrosion and monitoring the degree of silver tarnishing. For each case study, a supervised data set is constructed, teaching the algorithm to recognize as distinct a specified appearance characteristic (such as corrosion, metal etc.) by comparing it to the reconstruction coefficients of each pixel. The segmented information is visualized by a simplified colormap. The calculated results are afterwards verified by visible inspection from conservation-restoration experts. The method can segment surfaces with changes in micro-geometry, but it reaches its limitation on surfaces with minimal topography and high specularity.