Abstract: Ultrasound sound-speed tomography requires accurate picks of time-of-flights (TOFs) of transmitted ultrasound signals, however, manual picking on large datasets is time-consuming. An improved automatic TOF picker is taught based on the Akaike Information Criterion (AIC) and multi-model inference (model averaging), based on the calculated AIC values, to improve the accuracy of TOF picks. The automatic TOF picker of the present invention can accurately pick TOFs in the presence of random noise with average absolute amplitude of up to 80% of the maximum absolute synthetic signal amplitude. The inventive method is applied to clinical ultrasound breast data, and compared with manual picks and amplitude threshold picking. Test results indicate that the inventive TOF picker is much less sensitive to data signal-to-noise ratios (SNRs), and performs more consistently for different datasets in relation to manual picking. The technique provides noticeably improved image reconstruction accuracy.