The objective of this study was to establish multivariate models to estimate gravimetric carbonization yield (GCY), apparent relative density (ARD) and final carbonization temperature (FCT) of Eucalyptus charcoal by NIR spectroscopy. Eucalyptus clones from commercial plantations managed for energy purposes and pulp and paper production were used. Wood prismatic specimens with nominal dimensions of 25mmx 25mmx 80 mm were carbonized at final temperatures of 400 degrees C, 500 degrees C, 600 degrees C, and 700 degrees C. NIR spectra measured directly on 160 charcoal specimens were correlated with GCY and ARD values obtained through conventional laboratory analyzes. Principal component analysis (PCA), partial least squares regression (PLS-R) and partial least squares discriminant analysis (PLS-DA) were utilized based on spectral and experimental information. The NIRS technique associated with PLS-R was able to predict FCT and GCY presenting cross-validation coefficients (R(2)cv) between reference and estimated data of 0.96 and 0.85, respectively. It was not possible to predict ARD based on charcoal spectral signature. Cross-and independent validations presented similar statistics, confirming the capacity of NIR spectroscopy coupled with multivariate analyses for monitoring charcoal quality. Specimen classifications into carbonization temperature groups through PLS-DA obtained 100% correct classification, except for the 500 degrees C temperature (97.5%). These models are able to reliably estimate the gravimetric yield and final pyrolysis temperature of charcoal, an important parameter that can be used as a quality criterion for industrial applications. This work will serve as a reference for the development of new studies and applications of the NIR technique in the assessment of charcoal quality.