The purpose of this study was to analyze the possibility of utilizing known signal processing and machine learning algorithms to correlate environmental data to chicken vocalizations. The specific musing to be analyzed consist of not just one chicken's vocalizations but of a whole collective, it therefore becomes a chatter problem. There have been similar attempts to create such a correlation in the past but with singled out birds instead of a multitude. This study was performed on broiler chickens (birds used in meat production). One of the reasons why this correlation is useful is for the purpose of an automated control system. Utilizing the chickens own vocalization to determine the temperature, the humidity, the levels of ammonia among other environmental factors, reduces, and might even remove, the need for sophisticated sensors. Another factor that this study wanted to correlate was stress in the chickens to their vocalization. This has great implications in animal welfare, to guarantee that the animals are being properly take care off. Also, it has been shown that the meat of non-stressed chickens is of much better quality than the opposite. The audio was filtered and certain features were extracted to predict stress. The features considered were loudness, spectral centroid, spectral sparsity, temporal sparsity, transient index, temporal average, temporal standard deviation, temporal skewness, and temporal kurtosis. In the end, out of all the features analyzed it was shown that the kurtosis and loudness proved to be the best features for identifying stressed birds in audio.