Fourier transform near-infrared (FT-NIR) spectroscopy was attempted to determine pH, which is one of the key process parameters in solid-state fermentation of crop straws. First, near infrared spectra of 140 solid-state fermented product samples were obtained by near infrared spectroscopy system in the wavelength range of 10 000～4 000 cm-1, and then the reference measurement results of pH were achieved by pH meter. Thereafter, the extreme learning machine (ELM) was employed to calibrate model. In the calibration model, the optimal number of PCs and the optimal number of hidden-layer nodes of ELM network were determined by the cross-validation. Experimental results showed that the optimal ELM model was achieved with 10-40-1 topology construction as follows: Rp=0.961 8 and RMSEP=0.104 4 in the prediction set. The research achievement could provide technological basis for the on-line measurement of the process parameters in solid-state fermentation.