Like other consumer products such as automobiles, selective laser sintering (SLS) additive manufacturing (AM) systems age in they lifespan – degradations of system components and materials hinder consistent outcome of the manufacturing process. Despite significant tool development, laser interactions with light-color materials challenge in-situ monitoring to properly capture the complex process physics, and data analytics to fully understand process degradation does not yet exist. The objective of this presentation is to discuss a sensing and data processing towards consistent and repetitive AM in presence of system degradation. We present an optical design to image the interaction between laser and Polyamide 12 (PA12) powders at different stages of system and material degradation. Pioneering a data analytics with morphological image processing, field correction, and particle analysis on the developed database, we address key issues induced by contaminated optics, spatters, and smokes to isolate the heat-affected zone (HAZ) from the noisy gray-scale raw images. From there, we analyze the morphology of the extracted area and identify signatures for process defects such as balling, overheating, and lack of sintering. These defects are further used to characterize process disturbances in presence of system degradation, bridging the gap between spatial-resolved process monitoring and our ultimate goal of model-based control for a robust, high-throughput AM.