This paper is part of a series which illustrates how modern methods of mult
ivariate statistics can be used to solve, or illuminate, damage identificat
ion problems. The technique discussed here is Kernel Discriminant Analysis
(KDA), which can be used to assign damage classifications to measured data
vectors. The data discussed is experimental data from a ball bearing system
in an undamaged state and in four damage states. The classifiers are train
ed on the data after an initial pre-processing stage and also after a furth
er statistical dimension reduction. The results from KDA are compared with
a benchmark statistical method and with a neural network classifier.