Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades. In this paper, we used deep learning to predict a person's age on Chest X-Rays. Specifically, we trained a CNN in regression fashion on a large publicly available dataset. Moreover, for interpretability, we explored activation maps to identify which areas of a CXR image are important for the machine (i.e. CNN) to predict a patient's age, offering insight. Overall, amongst correctly predicted CXRs, we see areas near the clavicles, shoulders, spine and mediastinum being most activated for age prediction, as one would expect biologically. As CXR is the most commonly requested imaging exam, a potential use case for estimating age may be found in the preventative counselling of patient health status compared to their age-expected average, particularly when there is a large discrepancy between predicted age and the real patient age.