  ClassX is a project aimed at creating an automated system to classify X-ray
sources and is envisaged as a prototype of the Virtual Observatory. As a
system, ClassX integrates into a pipeline a network of classifiers and an
engine that searches and retrieves for a given target multi-wavelength
counterparts from the worldwide data storage media. It applies machine learning
methods to `train' different classifiers using different `training' data sets.
In ClassX, each classifier can make its own class (object type) assignment and
is optimized for handling different tasks and/or different object types. A user
would generally select a certain classifier to make, for instance, a most
complete list of candidate QSOs, but a different classifier would be used to
make a most reliable list of candidate QSOs. Still different classifiers would
be selected to make similar lists for other object types. Along with the class
name assignment, a network classifier outputs the probability for a source to
belong to the assigned class as well as probabilities that the source belongs
in fact to other classes. We illustrate the current capabilities of ClassX and
the concept of a classifiers network with the results obtained with classifiers
trained using ROSAT data. ~
