Accurate measurements of feeding behavior are essential for a reliable management and research of grazing ruminants. Monitoring grazing and rumination activities can indicate animals’ health and welfare, because ruminants have a daily chewing requirement to maintain a healthy rumen environment. In this application we propose novel methods to analyze and automatically recognize sound signals of chewing and biting in cows and sheep, including pasture species identification and dry matter intake estimation. These methods use appropriate acoustic representations and ad hoc statistical modeling for automatically segmenting and classifying acoustic ingestive behavior. Acoustic monitoring provides the most accurate quantification of chewing, and could be developed into a routine method to monitor animals like dairy cows, which are subject to the stresses of extremely high productivity.