Abstract: To promote the application of hyperspectral remote sensing technology to tobacco quality monitoring, experiments were conducted using different light quality to examine cv. Yunyan 87, the contents of plastid pigments (chlorophyll a, chlorophyll b and carotenoid) and their corresponding hyperspectral reflectances of flue-cured tobacco leaves at different growing stages. Hyperspectral reflectance data was reduced from 2151 to 21 by wavelet coefficient extraction, then the reduced inversion models based on back propagation neural network for predicting the contents of chlorophyll a, chlorophyll b, carotenoid in tobacco leaves were established with plastid pigments as output factors and reduced hyperspectral reflectance data as input factors. The training function adopted trainlm of L-M optimization algorithm, while the transfer functions of the input layer and output layer were tansig and purelin, respectively. The number of the hidden layer nodes in BP neural network models for chlorophyll a, chlorophyll b and carotenoid was 27, 32 and 45, respectively. The results showed that the determination coefficients of BP neural network models for chlorophyll a, chlorophyll b and carotenoid were 0.84, 0.86 and 0.76; the root mean square errors were 0.12, 0.14 and 0.10; the absolute mean relative errors were 0.23, 0.21, and 0.15, respectively. The three models well predict the contents of plastid pigments with high precisions and low errors.