In this study, the near infrared spectroscopy coupled with Back-Propagation (BP) network was used for the recognition of three kinds of plantation wood (Eucalyptus urophylla, Pinus massoniana, Populus×euramericana (Dode) Guineir cv. “San Martino” (1-72/58)). The study considered the effects of hidden layer neurons number, spectral pretreatment method and spectral regions on BP model, which are compared with SIMCA model simultaneously. The results showed that, (1) the recognition rate was 97.78% achieved by BP network model with hidden layer neurons number 13 and the spectral region of 780～2 500 nm. (2) BP model with spectral region of 780～2 500 nm was more robust than the other two BP models with spectral regions of 780～1 100 and 1 100～2 500 nm, of which recognition rates were 97.78%, 95.56% and 96.67%, respectively. After the full spectra was pretreated with the first derivative and the second derivative methods, the recognition rates of BP models fell down to 93.33% and 71.11%. However, the recognition rate of BP model rose to 98.89% with the full spectra being pretreated by the multiplicative scatter correction (MSC). (3) Compared with SIMCA models that recognition rates of three spectral regions (780～2 500, 780～1 100 nm, and 1 100～2 500 nm) were 76.67%, 81.11% and 82.22% respectively, BP network work models had higher recognition rates.