Pre-processing of near-infrared (NIR) spectral data has become a necessary part of chemometrics modeling and is widely used in many practical applications. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve subsequent qualitative or quantitative analysis. Herein, a localized version of standard normal variate (SNV) is proposed, in which the correction parameters are estimated from local spectral areas. The method of determining the optimal spectral segmentation is also presented. Compared with full range methods, the local method demonstrates advantages in spectral linearity correction, model interpretation and prediction accuracy. Several benchmark NIR data sets were studied in our experiments; the proposed method achieved comparable performance against proven full range methods, with the reduction of prediction errors being statistically significant in many cases. Display omitted •A local pre-processing algorithm is proposed for near-infrared spectra.•The optimal segmentation of local areas can be automatically determined by a cross validation scheme.•Experiments show that the proposed local method outperformed the full range pre-processing methods.•The proposed method has no manual parameter and can be easily used in many applications.