Near-IR (NIR) diffuse reflectance (DR) spectra and Fourier-transform (FT) Raman spectra were measured for 12 kinds of block and random poly(propylene) (PP) copolymers with different ethylene content in pellets and powder states to propose calibration models that predict the ethylene content in PP and to deepen the understanding of the NIR and Raman spectra of PP. Band assignments were proposed based calcn. of the second derivs. of the original spectra, anal. of loadings and regression coeff. plots of principal component anal. (PCA) and principal component regression (PCR) (predicting the ethylene content) models, and comparison of the NIR and Raman spectra of PP with those of linear low-d. polyethylene (LLDPE) with short branches. PCR and partial least squares (PLS) regression were applied to the second derivs. of the NIR spectra and the NIR spectra after multiplicative scatter correction (MSC) to develop the calibration models. After MSC treatment, the original spectra yield slightly better results for the std. error of prediction (SEP) than the second derivs. A plot of regression coeffs. for the PCR model shows peaks due to the CH2 groups pointing upwards and those arising from the CH3 groups pointing downwards, clearly sepg. the bands due to CH3 and CH2 groups. For the Raman data, MSC and normalization were applied to the original spectra, and then PCR and PLS regression were carried out to build the models. The PLS regression for the normalized spectra yields the best results for the correlation coeff. and the SEP. Raman bands at 1438, 1296, and 1164 cm-1 play key roles in the prediction of the ethylene content in PP. The NIR chemometric evaluation of the data gave better results than those derived from the Raman spectra and chemometric anal. Possible reasons for this observation are discussed.