Open AccessMultivariate statistical techniques were employed to study soil-vegetation interrelationships in a secondary forest of South-Southern Nigeria. The grid system of vegetation sampling was used to randomly collect vegetation and soil data from fifteen quadrats of 10m x 10m. The result of principal components analysis identified seven basic sets of soil-vegetation variables that enhanced the interrelationships. Canonical correlation result indicated a positive association between organic matter and tree size, while the linear association between organic matter and tree density revealed an inverse relationship. The result of redundancy coefficient indicated that 18 percent of the variance in vegetation characteristics was accounted for by the variability in soil properties whereas, 81 percent of the variance in soil properties was accounted for by the variability in vegetation characteristics. The regression analyses on the other hand indicated that exchangeable sodium positively influenced tree species composition and richness; and that tree size as well as tree density exerted substantial influence on the contents of organic matter and total nitrogen of the soil. However, drawing inference from results of canonical correlation analysis and those of multiple regression analysis, it was concluded that soil and vegetation components of the secondary forest vegetation were mutually dependent and therefore exerted joint influences on each another.