Emerging issues related to the sustainability aspects of livestock farms are nowadays of fundamental importance for an efficient and low-impact management. The facilities must increasingly respond to objectives of low environmental and social impact and the management techniques should ensure both animal welfare and high production. Then, priority must be given to both aspects of socio-economic interest and animal welfare and health. Since dairy cattle are in an intensive housing system for most of their lives, facilities have a significant impact on the animals’ welfare. Despite the growing interest in finding new animal housing and equipment management strategies for reducing impacts, and the interesting results mostly concerning the daily production data, there are lack of studies investigating the factors that can lead to productive anomalies. On the other hand, the use of automatic milking robots, milking parlors, collars and pedometers allows the precise monitoring of dairy cows, providing farmers with real time information. In this context, the early detection of production anomalies is fundamental for animal health and safety. In this work, a data driven approach for detecting milk production and behavior anomalies is presented. The DAIRY CHAOS procedure proposed in this paper bases integrates the assessment of two numerical algorithms having the scope of separately detect anomalies daily data for a single cow.