Abstract: In order to detect chicken hatching egg incubation during the early period, a laboratory hyperspectral imaging system was setup to capture hyperspectral transmission images of 90 hatching eggs on the first three days at the spectral region of 400～1 000 nm. Dimension reduction was implemented on hyperspectral data based on Independent Components Analysis (ICA) and 5 characteristic bands with 571、614、661、691 and 716 nm. Next, spectral average and standard deviation were extracted from each band, thus 10 characteristic variables in total for 5 characteristic bands were acquired. To remove the correlation between variables, Principal Component Analysis (PCA) was conducted on 10 characteristic variables, and 4 principal component variables were extracted as the input of the discrimination model constructed by Learning Vector Quantization (LVQ) artificial neural network. Verification experiments showed that discrimination model had good stability (cv 1.7) and achieved prediction accuracies of 78.8% on 1st day, 90.3% on 2nd day and 98.6% on 3rd day. This research demonstrates that the hyperspectral imaging technique is feasible for detecting hatching eggs incubation during the early hatching period.