Modem quantitative IR spectroscopy includes a wide repertoire of techniques including: K and P matrix methods, principal component regression (PCR), and partial least squares (PLS). The relative merits of these methods has been discussed in the literature, however their relative performance often depends on the specific chemical system studied. Further the relative performance of individual implementations of a single technique can vary. What is necessary is a common set of tools that can be used to examine every method in a consistent manner. This set of tools can then be used in selecting the most appropriate quantitative model for the specific chemical system studied. The use of Monte-Carlo methods as a common calibration selection tool, and as a calibration diagnostic tool is presented. For univariate calibrations, simple inspection of a calibration plot can be used to evaluate alternate calibration methods. Diagnostic information, such as the required instrumental precision and accuracy necessary for desired calibration tolerances, is a straightforward procedure. In the multivariate case, extracting the same information is less intuitive. Finding the limiting noise sources in multivariate calibrations is often not easy to determine. By applying gaussian noise to individual calibrations parameters, the degradation in calibration performance can be plotted as a function injected noise. These plots provide specific noise sensitivity information that is straightforward in interpretation even when the details of the underlying method are unknown. This approach is largely technique independent, allowing the comparison of dissimilar methods. The use of Monte-Carlo diagnostics are illustrated, for the major quantitative IR methods, using two different example data sets. The first is from a designed experiment quantitating xylene mixtures using precise gravimetric reference analysis. The second is a near infrared data set quantitating moisture and protein using standard reference methods. These data are used to demonstrate the utility of Monte-Carlo techniques in providing necessary information for reliable multivariate calibrations.