We developed the sliding window correlation procedure in order to examine populations for possible heterogeneity in the ways two variables are related with each other. This procedure involves computing correlation coefficients (R) for overlapping successive segments of the covariate scores. The distribution of resulting Rs reveals fluctuations in the degree and direction of R over the sample of ranked scores. This procedure is applied to behavioral data of aged rats, which were rank-ordered according to water maze performance, and correlated with open field exploration and conflict behavior in a light/dark chamber. Results revealed correlation coefficients of varying magnitudes and opposing directions for different segments of the population, which were obscured by overall correlation analysis. E.g. for the superior learners, the Rs were highest between maze learning ability, increased open field exploration and reduced anxiety in the conflict test, whereas for the intermediate learners the Rs were highest for maze learning ability related with reduced exploration and increased anxiety. Thus, the sliding window correlation distribution can be applied in conjunction with overall correlation analysis to provide information about the potential presence and locations of subgroups within a population, especially if overall correlation analysis does not yield significant results.