In order to increase the detecting accuracy and stability of the fertility of hatching eggs during early hatching period, information of vision and acoustic sensors were fused in the sensor level on the fifth day of incubation, and two different artificial neural networks were chosen to establish models for detecting fertility of hatching eggs. Results showed that the sensor fusion model by LVQ artificial neural network obtained a higher discriminating accuracy and stability than the sensor fusion model of BP artificial neural network. The discriminating accuracy of hatching eggs during the early hatching period was up to 92% and 88% by computer vision technique and impact excitation technique, respectively. However, the discriminating accuracy reached 98% by sensor fusion model, which implied that the sensor fusion was feasible for detecting fertility of hatching eggs during early hatching period. 