A newly devised fuzzy metric for measuring the dissimilarity between two planar chromatographic profiles is proposed in this paper. It does not require an accurately assigned sample-feature matrix and can cope with slight imprecision of the positional information. This makes it very suitable for 1-D techniques which do not have a second spectroscopic dimension to aid variable assignment. The usefulness of this metric has been demonstrated on a large data set consisting of nearly 400 samples from Denaturing Gradient Gel Electrophoresis (DGGE) analysis of microbes on human skin. The pattern revealed by this dissimilarity metric was compared with the one represented by a sample-feature matrix and highly consistent results were obtained. Several pattern recognition techniques have been applied on the dissimilarity matrix based on this dissimilarity metric. According to rank analysis, within-individual variation is significantly less than between-individual variation, suggesting a unique individual microbial fingerprint. Principal Coordinates Analysis (PCO) suggests that there is a considerable separation between genders. These results suggest that there are specific microbial colonies characteristic of individuals.