Accurate segmentation of breast region is an essential step for quantitative analysis of breast parenchyma on mammograms. Pectoral muscle identification on mediolateral oblique (MLO) view mammograms remains a challenging problem. In this study, our purpose is to develop a supervised deep learning approach for automated identification of the pectoral muscle on MLO-view mammograms. With IRB approval, 756 MLO-view mammograms including 656 digitized film mammograms (DFM) and 100 full field digital mammograms (DM) were retrospectively collected. The film mammograms were digitized at a pixel size of 50 μm × 50 μm and the DMs were acquired with a GE Senographe system with a pixel size of 100 μm × 100 μm. All mammograms were subsampled to 800 μm × 800 μm before the pectoral muscle analysis. An experienced radiologist manually segmented the pectoral muscle boundary as the reference standard. We constructed a U-Net-like deep convolutional neural network (DCNN) to identify the boundary of the pectoral muscle. The DCNN consisted of a contracting path to capture multi-resolution image context and a symmetric expanding path for prediction of the pectoral muscle region. A total of 15 million parameters in DCNN were trained with a mini-batched gradient decent algorithm by minimizing a binary cross-entropy cost function. Ten-fold crossvalidation was used in training and evaluating the performance of our model. The DCNN-segmented pectoral muscle was compared to the reference standard with three criteria: 1) the percent overlap area (POA), 2) the Hausdorff distance (Hdist) and 3) the average Euclidean distance (AvgDist). We found that the mean POA, the mean Hdist, and the mean AvgDist were 96.0±5.3%, 2.14±1.50 mm, and 0.77± 0.97 mm, respectively. Further study is underway to evaluate its effect on quantitative analysis of mammograms.