A method for the spectrometric characterization of microorganisms, comprising: - Providing a test microorganism whose identity is known on a first taxonomic level; - Sterilizing the test microorganism; - Acquiring spectrometric measurement data from the sterilized test microorganism under conditions which allow the influence of at least one source of variance that is not based on the taxonomic classification of the test microorganism; - Selecting a classifier which is trained to determine the identity of a microorganism on a second taxonomic level which is subordinate to the first taxonomic level, where possible identities of the classifier on the second taxonomic level are assigned to the known identity of the test microorganism on the first taxonomic level, and - Applying the classifier to the measurement data in order to determine the identity of the test microorganism on the second taxonomic level; - wherein the classifier is variance-conditioned by obtaining it through training on target-edly variance-loaded spectrometric reference data of different known reference microorganisms which exhibit the same identity as the test microorganism on the first taxonomic level and cover different identities on the second taxonomic level, where the training includes the stipulation of giving greater weighting to those spectral characteristics of a first type from the reference data which promote the differentiation of the different identities on the second taxonomic level, than to those spectral characteristics of a second type from the reference data which are affected by the targeted variance, in order to largely or completely mask out the effect of variance in the characterization of the test microorganism on the second taxonomic level. - Providing a test microorganism whose identity is known on a first taxonomic level; - Sterilizing the test microorganism; - Acquiring spectrometric measurement data from the sterilized test microorganism under conditions which allow the influence of at least one source of variance that is not based on the taxonomic classification of the test microorganism; - Selecting a classifier which is trained to determine the identity of a microorganism on a second taxonomic level which is subordinate to the first taxonomic level, where possible identities of the classifier on the second taxonomic level are assigned to the known identity of the test microorganism on the first taxonomic level, and - Applying the classifier to the measurement data in order to determine the identity of the test microorganism on the second taxonomic level; - wherein the classifier is variance-conditioned by obtaining it through training on target-edly variance-loaded spectrometric reference data of different known reference microorganisms which exhibit the same identity as the test microorganism on the first taxonomic level and cover different identities on the second taxonomic level, where the training includes the stipulation of giving greater weighting to those spectral characteristics of a first type from the reference data which promote the differentiation of the different identities on the second taxonomic level, than to those spectral characteristics of a second type from the reference data which are affected by the targeted variance, in order to largely or completely mask out the effect of variance in the characterization of the test microorganism on the second taxonomic level. <EOS>