Several methods of aggregating outcomes of classifiers are applied to artificial and real magnetic resonance MR spectra. Logistic regression, linear combination of classifiers, fuzzy integration, stacked generalization and some other fusion methods, as well as different ways of estimating necessary parameters, are considered. The results indicate that the fusion of classifiers improves the performance of the individual classifiers. On real MR spectra, which are characterized by the paucity of experimental data and low signal-tonoise ratio, the results vary. Some methods perform well on some data sets and poorly on others. Strategies are recommended to gain from classifier aggregation.