The goal of the present study was to evaluate techniques for modeling the physiological responses, rectal temperature, and respiratory rate of black and white Holstein dairy cows. Data from the literature (792 data points) and obtained experimentally (5884 data points) were used to fit and validate the models. Each datum included dry bulb air temperature, relative humidity, rectal temperature, and respiratory rate. Two models based on artificial intelligence—artificial neural networks and neurofuzzy networks—and one based on regression were evaluated for each response variable. The adjusted models predict rectal temperature and respiratory rate as a function of dry-bulb air temperature and relative humidity. These models were compared using statistical indices. The model based on artificial neural networks showed the best performance, followed by the models based on neurofuzzy networks and regression; the last two performed similarly.