Spectral signal intensities, especially in 'real-world' applicationswith nonstandardized samplepresentationduetouncontrolled variables/factors, commonly require additional spectral processing to normalize signal intensity in an effective way. In this study,we have demonstrated the complexity of choosing a normalization routine in the presence ofmultiple spectrally distinct constituents by probing a dataset of Raman spectra. Variation in absolute signal intensity (90.1% of total variance) of the Raman spectra of these complex biological samples swamps the variation in useful signals (9.4% of total variance), degrading its diagnostic and evaluative potential. Using traditional spectral band choices, it is shown that normalization results aremore complex than generally encountered in traditionally designed sample sets investigating limited chemical species. We demonstrate that no choice of a single band proves to be appropriate for predicting all the reference parameters, instead requiring a tailored normalization routine for each parameter. Of the reference parameters studied in the chosen system, signals from pathogenic adducts in ocular tissues called advanced glycation endproducts were most prominent when normalizing about the 1550-1690 cm-1 region of the spectrum (17.5%of total variance, compared with 0.3%for unnormalized), while prediction of pentosidine and gender were optimized by normalization about the 1570 (R 2 = 0.97 vs 0.57 for unnormalized) and1003 cm-1 (p<0.0000001 vsp<0.01 for unnormalized) bands, respectively. The data obtained point to the extreme sensitivity of multivariate analysis to signal intensitynormalization. Some general guidelines formaking appropriate band choices are given, including the use of peak-finding routines. Copyright © 2008 John Wiley & Sons, Ltd.