The usability of the constructive neural algorithms as pattern classif
iers is investigated. It is pointed out that the unboundedness of the
decision regions formed by most neural recognizers leads to substantia
l limitations of the generalization capabilities of these nets. We spe
cify a constructive neural recognizer that forms bounded decision regi
ons, and report the results of this algorithm on a series of benchmark
problems that resemble the usual pattern recognition problems.