The need for understanding the human thermoregulatory system has increased and so has the importance of developing accurate dynamic models for the thermoregulatory system. Historically, there have been mainly two modeling approaches for the human thermoregulatory system--theoretical and empirical. However, there are limitations to both of these approaches. The complexity and the lack of knowledge of the physiological behavior of the human body limit the theoretical approach. The empirical approach is unsuitable for practical use, due to the large amount of data requirements. This study is unique in the sense that it utilizes a new methodology that can develop accurate compact closed-from predictive models from a small amount of data. This work explores a new predictive modeling methodology developed for engineering processes that approximate the block-oriented Hammerstein structure. It is called the Hammerstein Block-oriented Exact Solution Technique (H-BEST) and is based on a compact, continuous-time, closed form solution that gives optimal (i.e., the smallest possible number) parameterization and preserves the form of the static gain functions. The approach that this study will take is to use the H-BEST for modeling of the skin temperature and sweat rate, by using the Wissler (1964) computer program as a surrogate human from an optimal (i.e. minimal) amount of experimental trials. The H-BEST models will help in addressing two major challenges faced when modeling the human thermoregulatory system. First, the H-BEST solution for the output responses will be used to significantly reduce the number of experimental trials per subject. Secondly, the H-BEST models will be used in optimal experimental design to significantly reduce the required time a subject needs to be in the environmental chamber for data collection. Statistical design of experiments and D-optimal criterion is used to solve these two problems. This study shows that there is much promise in using H-BEST when modeling human subjects.