This paper addresses and deals with the problem of automating condition monitoring of wood in the transportation domain. Current day condition monitoring applications involving wood are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out. These inspections are mostly done intuitively by skilled personnel. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Data resulting from impact acoustics tests made on wooden beams has been used. The relation between condition of the wooden beam and their respective emissions has been analyzed experimentally applying different feature extraction techniques. Combining the usage of traditional frequency extraction techniques like the magnitude of the signal together with famous speech recognition techniques like Cepstral Coefficients, Linear Predictive Coding yield good results. Effect of using classifiers like Gaussian Mixture Models and Learning Vector Quantization has been tested and compared. In the current case Gaussian mixture model seem to achieve higher classification rates than Learning Vector Quantization model.