Tracking can be defined as the tendency of individuals or collections of individuals to stay within a particular course of growth over time relative to other individuals. Thus, tracking describes stability in growth patterns. This paper outlines a statistical procedure for examining tracking in a single sample of measurements made on humans or other animals. This nonparametric procedure, based on Cohen's (1960) kappa statistic, is suitable for equally or unequally spaced serial data that is complete and is appropriate for questions concerning growth as well as other time-dependent phenomena. It is a conceptually simple longitudinal method that affords insight regarding the predictability of growth within a population. For example, by tracking, one can ask if young children who are in the lowest height for age category are likely to end up in that category at an older age. A user-friendly GAUSS program is provided that generates overall as well as individual and track-specific statistics. High-resolution graphic representations of the data are also generated by the program. Examples are presented, including a tracking analysis of Guatemalan Indian children using quartiles.