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This study was performed to classify the acoustic emission (AE) signal due to surface cracking and moisture movement in the flat-sawn boards of oak (Quercus Variablilis) during drying using the principal component analysis (PCA) and artificial neural network (ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count, event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 % of the variance of AE parameters could be accounted for by the first and second principal components. An ANN analysis was successfully used to classify the AE signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.