Cross laminated timber (CLT) panels, which are used as load bearing plates and shear panels in timber structures, can serve as roofs, walls and floors. Since timber is construction material with relatively less stiffness, the design of such structures is often driven by serviceability criteria, such as deflection and vibration. Therefore, accurate vibration and elastic properties are vital for engineered CLT products. The objective of this research is to explore a method to determine the natural frequencies of orthotropic wood plates efficiently and fast. The method was developed based on vibration signal processing by wavelet to acquire the effective sample data, and a model developed by artificial neural network (ANN) to achieve the prediction of nature frequencies. First, experiments were performed to obtain vibration signals of single-layer plates. The vibration signals were then processed by wavelet packet transform to extract the eigenvectors, which served as the samples to train the ANN model. The trained model was employed to predict three nature frequencies of other test specimens. The results showed that the proposed method can produce predicted frequencies fast and effi- ciently within 10% of the measured values.