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We aim to extend and test the classifiers presented in a previous workagainst an independent dataset. We complement the assessment of the validity ofthe classifiers by applying them to the set of OGLE light curves treated asvariable objects of unknown class. The results are compared to publishedclassification results based on the so-called extractor methods.Twocomplementary analyses are carried out in parallel. In both cases, the originaltime series of OGLE observations of the Galactic bulge and Magellanic Cloudsare processed in order to identify and characterize the frequency components.In the first approach, the classifiers are applied to the data and the resultsanalyzed in terms of systematic errors and differences between the definitionsamples in the training set and in the extractor rules. In the second approach,the original classifiers are extended with colour information and, again,applied to OGLE light curves. We have constructed a classification system thatcan process huge amounts of time series in negligible time and provide reliablesamples of the main variability classes. We have evaluated its strengths andweaknesses and provide potential users of the classifier with a detaileddescription of its characteristics to aid in the interpretation ofclassification results. Finally, we apply the classifiers to obtain objectsamples of classes not previously studied in the OGLE database and analyse theresults. We pay specific attention to the B-stars in the samples, as theirpulsations are strongly dependent on metallicity.

Automated supervised classification of variable stars II. Application to the OGLE database