Steel can be produced from vegetable charcoal used as a thermo reducer in steel blast furnaces. Hence, the metallurgical industry requires new solutions for controlling the quality of their charcoal in order to maintain the same quality standard. The objective of this study was to assess the properties of charcoal by near infrared spectroscopy. Partial least square (PLS) regressions were established for estimating fixed-carbon, volatile matter content and gravimetric yield of charcoal. These models were validated by cross-validations and independent test sets and presented similar performance. PLS-R models obtained from untreated NIR spectra for FCC and VMC yielded promissory statistics [r2p of ~0.9; root mean squared error of prediction (RMSEP) of ~3.3%; and ratio of the standard error of prediction to standard deviation (RPD)>3] while the statistics of the calibrations for gravimetric yield had relatively lower magnitude (R2p of ~0.8, a RMSEP of ~2.2 and RPD<2). Since new solutions are required for controlling the quality of charcoal with speed and precision, these models can be useful for monitoring charcoal quality in steel industries.