  The luminosity changes of most types of variable stars are correlated in the
different wavelengths, and these correlations may be exploited for several
purposes: for variability detection, for distinction of microvariability from
noise, for period search or for classification. Principal component analysis is
a simple and well-developed statistical tool to analyze correlated data. We
will discuss its use on variable objects of Stripe 82 of the Sloan Digital Sky
Survey, with the aim of identifying new RR Lyrae and SX Phoenicis-type
candidates. The application is not straightforward because of different noise
levels in the different bands, the presence of outliers that can be confused
with real extreme observations, under- or overestimated errors and the
dependence of errors on the magnitudes. These particularities require robust
methods to be applied together with the principal component analysis. The
results show that PCA is a valuable aid in variability analysis with multi-band
data.
