A self-built acoustic response system was used to non-destructively detect the eggshells cracks when the eggs were artificially cracked to produce various kinds of cracks.In order to minimize the dependence on the position cracked,the knocking point was always located near the eggshell equator.The original acoustic signals were preprocessing to detect endpoints of signals and then analyzed with HilbertHuang transformation (HHT) and multiply principal component analysis (MPCA).Results showed that the accuracy of detection and classification of egg with cracks is very well via a support vector machine (SVM) and artificial neural network(ANN) models since the main features of the time domain and frequency domain were extracted from HHT and Multi-PCA when the cracks have randomly distribution.The radial basis kernel function is best for SVM and the accuracy for crack classification can be up to 90%.The whole regression coefficient is 0.924 76 with the accuracy of 86.70% for ANN model.It is indicated that the analytical methods of HHT and Multi-PCA are very suitable for detecting the cracked eggshell.