Condition monitoring of machine health via analysis of vibration, acoustic and other signals offers an important tool for reducing the machine downtime and maintenance costs. The key aspect in this process is the ability to relate features derived from the recorded sensor signals to the physical condition of the monitored asset in real time. This paper uses simple machine learning techniques to examine the ability of specific time-domain features obtained from vibration signals to predict the progression of surface distress in lubricated, rolling-sliding contacts, such as those found in rolling bearings and gears. Controlled experiments were performed on a triple-disc rolling contact fatigue rig using seeded-fault roller specimens where micropitting damage was generated and its progression directly observed over millions of contact cycles. Vibration signals were recorded throughout the experiments. Features known as condition indicators were then extracted from the recorded time-domain signals and their evolution related to the observed physical state of the associated specimens using simple machine learning techniques. Five time-domain condition indicators were examined, peak-to-peak, root-mean-square, kurtosis, crest factor and skewness, three of which were found not to be redundant. First, a classification model using KNN nearest neighbor was built with the three informative condition indicators as training data. The cross-validation results indicated that this classifier was able to predict the presence of micropitting damage with a relatively high precision and a low rate of false positives. Secondly, a k-means clustering analysis was performed to measure the significance of each condition indicator by leveraging patterns. The peak-to-peak condition indicator was found to be a good predictor for progression of micropitting damage. In addition, this indicator was able to distinguish between micropitting and pitting failure modes with a high success rate. Finally, the condition indicator response was correlated with the predicted damage state of the test specimen obtained through an existing physics-based surface distress model in order to illustrate the potential of hybrid models for improved prognostics of damage progression in rolling-sliding tribological contacts.