This study was aimed at developing a method for automatically selecting a few representative frames from several hundred axial-shear strain elastogram frames typically obtained during freehand compression elastography of the breast in vivo. This may also alleviate some inter-observer variations that arise at least partly because of differences in selection of representative frames from a cine loop for evaluation and feature extraction. In addition to the correlation coefficient and frame-average axial strain that have been previously used as quality indicators for axial strain elastograms, we incorporated the angle of compression, which has unique effects on axial-shear strain elastogram interpretation. These identified quality factors were computed for every frame in the elastographic cine loop. The algorithm identifies the section having N contiguous frames (N = 10) that possess the highest cumulative quality scores from the cine loop as the one containing representative frames. Data for total of 40 biopsy-proven malignant or benign breast lesions in vivo were part of this study. The performance of the automated algorithm was evaluated by comparing its selection against that by trained radiologists. The observer- identified frame that consisted of a sonogram, axial strain elastogram and axial-shear strain elastogram was compared with the respective images in the frames of the algorithm-identified section using cross-correlation as a similarity measure. It was observed that there was, on average (∼standard deviation), 82.2% (∼2.2%), 83.4% (∼3.8%) and 78.4% (∼3.6%) correlation between corresponding images of the observer-selected and algorithm-selected frames, respectively. The results indicate that the automatic frame selection method described here may provide an objective way to select a representative frame while saving time for the radiologist. Furthermore, the frame quality metric described and used here can be displayed in real time as feedback to guide elastographic data acquisition and for training purposes. © 2016 World Federation for Ultrasound in Medicine & Biology