We present a method for the photometric selection of candidate quasars in multiband surveys. The method makes use of a priori knowledge derived from a subsample of spectroscopic confirmed quasi-stellar objects (QSOs) to map the parameter space. The disentanglement of QSOs candidates and stars is performed in the colour space through the combined use of two algorithms, the probabilistic principal surfaces and the negative entropy clustering, which are for the first time used in an astronomical context. Both methods have been implemented in the voneural package on the Astrogrid Virtual Observatory platform. Even though they belong to the class of the unsupervised clustering tools, the performances of the method are optimized by using the available sample of confirmed quasars and it is therefore possible to learn from any improvement in the available 'base of knowledge'. The method has been applied and tested on both optical and optical plus near-infrared data extracted from the visible Sloan Digital Sky Survey (SDSS) and infrared United Kingdom Infrared Deep Sky Survey-Large Area Survey public data bases. In all cases, the experiments lead to high values of both efficiency and completeness, comparable if not better than the methods already known in the literature. A catalogue of optical candidate QSOs extracted from the SDSS Data Release 7 Legacy photometric data set has been produced.