Individual records from the coding of molecular polymorphism (molecular profiles) are particularly useful for the identification of clones or cultivars, in pedigree analysis, in the estimation of genetic distances and relatedness, and as a tool in genome mapping and population genetics. A parametric statistical analysis of molecular profile components can be infeasible because of the huge number of observed markers, the presence of missing values and the high number of parameters required to evaluate the importance of interactions among markers. Moreover, new powerful molecular techniques make possible the analysis of numerous markers at one time; therefore parametric statistical methods could result in troublesome models with more parameters than data. The field of computer-based techniques offers new strategies to cope with the complexity of molecular profiles. We suggest the use of a Genetic Classifier System to evaluate the importance of profile components. The procedure is based on a Genetic Algorithm approach, a numerical technique that simulates some features of the natural selection process to solve problems. A set of isozyme data from a Norway spruce population is analysed in order to assess their ability to predict the individual plant response to the presence of abiotic stresses. The results, obtained by three different computer simulations, show that this computer-based approach is particularly effective for ranking profile components according to their relevance. Genetic Classifier Systems could also be used as a preliminary step to reduce the complexity of molecular data sets.