Radiologists often compare sequential radiographs side-by-side in order to identify regions of change and evaluate the clinical significance of such regions. Some changes in pathology, however, may be overlooked or misinterpreted. For this reason, temporal subtraction (TS) images provide an important tool for enhanced visualization. Not all areas of “change” demonstrated on a TS image, however, are caused by pathology. The purpose of this study was to develop an automated computer-aided diagnosis (CAD) system to locate regions of change in TS images as well as to classify such regions as being true regions of pathologic change or false regions of change caused by misregistration artifacts. The dataset used in this study contained 120 images, on which an experienced radiologist outlined 74 regions of true pathologic change that were used as the gold standard. Through gray-level thresholding and initial false-positive reduction, an initial set of candidates was extracted and inputted into a classifier. A five-fold cross-validation method was employed to create training and testing groups. Both false-candidate regions as well as the gold-standard regions were used as training data. Of the three classifiers tested (support vector machine, logistic regression, and linear discriminant analysis), the logistic regression classifier performed the best with a sensitivity of 96% and specificity of 84%; receiver operating characteristic (ROC) analysis resulted in an area under the ROC curve of 0.94. These results show promise in the performance of the CAD system to detect regions of pathologic changes in TS images of the chest.