The objective of this dissertation was to examine associations of lameness and health disorders with milk production and behavior of cows in herds with automated (robotic) milking systems (AMS). The first study was focused on lameness and examining herd- and cow-level factors associated with lameness, productivity, and behavior. At the herd level, the two main factors associated with productivity and behavior were lameness prevalence and stocking density. The prevalence of lameness (either clinical or severe) was negatively associated with environmental temperature and the frequency of manure alley scraping, and was positively associated with stocking density and curb height of lying stalls. At the cow level, factors associated with lameness were lower body condition, higher parity, and lower environmental temperature. When accounting for other cow-level factors, lame cows produced less milk in fewer milkings each day, were more likely to be fetched, and spent more time lying down in bouts which were longer, compared to non-lame cows. In a second study, cows were monitored longitudinally and data were analyzed retrospectively relative to the day diagnosis of multiple health disorders; deviations in productivity, body weight, rumination time, and a commercial measure of activity occurred several days to 2 wk before diagnosis. In the final study, a larger sample of cows were similarly monitored in terms of behavior, production, and health status. Acute health disorders were associated with noticeable deviations from those cows’ baseline AMS data occurring 4 to 12 d before diagnosis, and rumination time often declined 1 d prior to milk yield. More chronic, state-like disorders were associated with significant, but subtle, longer-term changes in productivity and behavior. In summary, this dissertation provides evidence that the behavior and productivity of cows in AMS herds differ between healthy cows and those afflicted with lameness and health disorders. These data provide much-needed information to inform the development and validation of AMS-specific alerts for lameness and health disorders.