Identification of wood species is an important part of wood processing and commerce. The traditional methods of wood species identification mainly include microscopic detection and wood texture recognition which are complex, time-consuming and costly. They cannot meet the current needs. Near infrared spectroscopy (NIRS) of wood combined with pattern recognition methods were used to identify wood species. NIRS combined with three kinds of pattern recognition methods including principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and soft independent modeling of class analogy (SIMCA) were used to identify fifty-eight wood species. Five spectral preprocessing methods including 5 point smoothing, standard normal variable (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first derivative (SG 1st-Der) and wavelet derivative (WD) were used to spectral transform. The correct recognition rate (CRR) of calibration and test sets were used for evaluation index of models. The results showed that the wood species could not be identified by using the first three principal components. In PLSDA model, the CRR values of calibration and test sets for original spectra model were the highest, which were 88.2% and 88.2%, respectively. The CRR values of calibration and test sets for 5 points smoothing model were 88.1% and 88.2%. The CRR values of calibration and test sets for SNV model were 84.4% and 84.5%. The CRR value of calibration and test sets for MSC model were 83.1% and 84.2%. The CRR values of calibration and test sets for SG 1st-Der model were 81.8% and 82.7%. The CRR values of calibration and test sets for WD (the wavelet basis is “Haar” and the decoposition scale is 80) model were 87.3% and 87.2%. In PLSDA models, the original spectra model had the best results compared to others. In SIMCA model, the CRR values of calibration and test sets for original spectra were 99.7% and 99.4%. The CRR values of calibration and test sets for 5 points smoothing were 100% and 100%. The CRR values of calibration and test sets for SNV model were 99.5% and 99.1%. The CRR values of calibration and test sets for MSC model were 99.0% and 98.4%. The CRR values of calibration and test sets for SG 1st-Der model were 98.4% and 99.0%. The CRR values of calibration and test sets for WD model were 100% and 100%. Compered to others spectra processed by 5 points smooting and WD had a best results in SIMCA models, the CRR values of calibration and test sets were 100%. Three kinds of pattern recognition methods combined with five spectral preprocessing methods were used to classify 58 kinds of wood. It could be concluded that the PCA method can’t explicitly classify 58 wood species because of complex properties of wood leading to the scatters of each wood species interwined with each other in PCA distribution diagram. The PLSDA model of original spectra could get a better result with the CRR value of 88.2% and 88.2% for calibration and test sets, respectively. The best SIMCA models were constructed by 5 point smoothing or WD preprocessing methods with the CRR of 100% for calibration and test sets. However, the factor of the WD-SIMCA model was smaller than 5 point smoothing method, and the model was more parsimonious, so WD-SIMCA model was an optimal model. The paper showed that spectral preprocessing methods can improve the accuracy of identificationof wood species, and SIMCA supervised pattern recognition method can be used to build effective identifying model and NIR combined with pattern recognition method can provide a rapid and simple method for identification of wood species.