Raman spectroscopy based discrimination of cervical precancer and normal tissue has been shown previously in vivo with fiber probe based measurements of colposcopically selected sites. With a view to developing in vivo large area imaging, macro raster scans of native cervical cone biopsies with an average of 200 spectra per sample are implemented (n=16 ). The diagnostic performance is evaluated using histopathological mapping of the cervix surface. Different data reduction and classification methods (principal component analysis, wavelets, k-nearest neighbors, logistic regression, partial least squares discriminant analysis) are compared. Using bootstrapping to estimate confidence intervals for sensitivity and specificity, it is concluded that differences among different spectra classification procedures are not significant. The classification performance is evaluated depending on the tissue pathologies included in the analysis using the average performance of different classification procedures. The highest sensitivity (89%) and specificity (86%) is obtained for the discrimination of normal squamous epithelium and high-grade precancer. When other non-high-grade tissue sites, such as columnar epithelium, metaplasia, and inflammation, are included, the diagnostic performance decreases.