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We present the first results of our dedicated programme of automatisedclassification of galaxies, stars and quasars in the mid-infrared all-sky datafrom the WISE survey. We employ the Support Vector Machines (SVM) algorithm,which defines a hyperplane separating different classes of sources in amultidimensional space of arbitrarily chosen parameters. This approach consistsof four general steps: 1) selection of the training sample, 2) selection of theoptimal parameter space, 3) training of the classifier, 4) application totarget data. Here, as the training set, we use sources from a cross-correlationof the WISE catalogue with the SDSS spectroscopic sample. The performance ofthe SVM classifier was tested as a function of size of the training set,dimension of the parameter space, WISE apparent magnitude and Galacticextinction. We find that our classifier provides promising results already forthree classification parameters: magnitude, colour and differential aperturemagnitude. Completeness and purity levels as high as 95% are obtained forquasars, while for galaxies and stars they vary between 80-95% depending on themagnitude, deteriorating for fainter sources.

Automatised classification of WISE sources: first results, future prospects