The feasibility of Fourier transform near infrared (FT-NIR) spectroscopic technology for rapid quantifying pear internal quality in different growing stage was investigated. A total of 248 pear samples collected at different harvest time (pre-harvest, mid-harvest and late harvest time) were used to develop the calibration models. The quality indices included soluble solids content (SSC) and titratable acidity (TA). Partial least squares (PLS) regression and principle component regression (PCR) regression were carried out describing relationships between the data sets of laboratory data and the FT-NIR spectra. Besides cross and test set validation, the established models were subjected to a further evaluation step by means of additional pear samples with unknown internal quality. Models based on the different spectral ranges and with several data pre-processing techniques (smoothing, multiplicative signal correction, standard normal variate, etc), were also compared in this research. Performance of different models was assessed in terms of root mean square errors of prediction (RMSEP) and correlation coefficients (r) of validation set of samples. The best predictive models had a RMSEP of 0.320, 0.019 and correlation coefficient (r) equal to 0.93, 0.89 for SSC and TA, respectively. Results indicated that FT- NIR spectroscopy could be an easy to facilitate, reliable, accurate and fast method for non-destructive evaluation of pears maturity.