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This study develops a high performance grading process based on the analysis of acoustic vibrations in the audible frequency range. The unique feature of the method is that the spectrum is directly applied to obtain predictive variables for estimating the modulus of elasticity and modulus of rupture. A partial least squares regression was used. This powerful method represents a compromise between principal component regression and multi-linear regression. Partial least squares regression screens for factors which account for the variance in the predictor variables and achieves the best correlation between factors and predicted variable. The method is based on projections, similar to principle components regression, whereby a set of correlated variables is compressed into a smaller set of uncorrelated factors.