There is increasing use of spectroscopic techniques, such as high-resolution NMR spectroscopy, to examine variations in cell metabolism and/or structure in response to numerous physical, chemical, and biological agents. In these types of studies, in order to obtain relative quantitative information, a comparison between signal intensities of control samples and treated or exposed ones is often conducted. The methods thus far developed for this purpose are not directly related to the overall intrinsic properties of the samples, but rather to the addition of external substances of known concentrations or to indirect measurement of internal substances. Another possibility is to estimate, by an opportune algorithm, a normalization constant which takes into consideration all cell metabolites present in the sample. Recently, a new normalization algorithm, based on Principal Component Analysis (PCA), was presented. PCA is a well-known statistical technique for analysis of large, multivariate datasets, which extracts the basic features of the data. The PRICONA (PRincipal COmponent Normalization Algorithm) algorithm use PCA in a new totally different manner: PCA is, in fact, used to normalize spectra in order to obtain quantitative information about the treatment effects. In this paper, it is shown that PRICONA can be used in the time domain, that is on NMR FIDs (Free Induction Decay) instead of on NMR spectra. That is advantageous because NMR FIDs do not require any operator dependent manipulation. The algorithm was tested by Monte Carlo simulations of NMR FIDs.