The potentially fast data acquisition capability and high resolution of the Fourier transform infrared (FTIR) spectrometer makes it an attractive tool for in-situ analysis of CVD systems. However, real-time extraction of useful quantitative information from the spectra recorded at medium (4 cm-1) or high (0.5 cm-1) resolution can be a difficult and time-consuming process. In this study, we evaluate the performance of various chemometric models used to decompose spectral data recorded during a quantitative study of the dimethyltin dichloride (DMTC) and oxygen (tin oxide) CVD process. The chemometric models investigated were classical least squares (CLS), principal component analysis (PCA), principal component regression (PCR), and partial least squares (PLS). Preliminary findings indicate that the PLS-1 algorithm is the most promising candidate for accurately determining individual constituent concentrations from the complicated gas mixture inherent in this CVD system.