In this paper, we present a study of dimensional reduction techniques for structural damage assessment of time-varying structures under uncertainty. Discrete tracking of the frequency response and the mode shape curvature index method is employed to perform damage assessment. Assessment of spontaneous damage in deteriorating structures is important as it can have potential benefits in improving their safety and performance. Most of the available damage assessment techniques incorporate the usage of system identification and classification techniques for detecting damage, location, and/or severity; however, much work is needed in the area of dimensional reduction in order to compress the ever-increasing data and facilitate decision-making in damage assessment classification. A comparison of dimensional reduction techniques is presented and evaluated in terms of separating damaged from undamaged data sets under two types of uncertainty, structural deterioration and environmental uncertainties. The use of a recursive principal component analysis for detecting and tracking structural deterioration and spontaneous damage is evaluated via computational simulations. The results of this study reveal that dimensional reduction techniques can greatly enhance structural damage assessment under uncertainties. This paper compares multiple dimensional reduction techniques by identifying their weaknesses and strengths.