This paper presents a comparative study of various trend detection methods developed using fuzzy logic, statistical, and regression techniques. A new method that uses noise rejection fuzzy clustering is also proposed in the paper to enhance the performance of trend detection methodologies. The comparative investigation has produced systematic guidelines for the selection of a proper trend detection method for different application requirements. This paper has resulted from work on military applications of on-line trend analysis, such as monitoring of wounded soldiers by first-response medical staff at the battlefield and high-acceleration protection of fighter jet pilots. Efficient trend detection methods can provide early warnings, severity assessments of a subject’s physiological state, and decision support for firstresponse medical attendants. Representative physiological variables such as blood pressure, heartbeat rate, and ear opacity are considered in this paper.