In this work, a pattern recognition technique has been proposed to automate the process of investigating the condition of wooden railway sleeper. Condition monitoring of wooden railway sleeper is mainly performed by visual inspection and also some impact acoustics tests are manually done. Though the manual procedure uses non-destructive testing methods (visual and sound analysis), decision making is largely based on intuition; moreover the process is rather slow, expensive and also requires skilled and trained staff. Impact acoustic signals have been collected from wooden railway sleepers for the purpose of achieving automation. Given the non-stationary nature of such impact acoustics signals, emphasis in this work has only been laid on non-stationary feature extraction techniques such as Short-Time Fourier Transform and Discrete Wavelet Transform. With the help of these techniques the signals can be analyzed in both time and frequency domains. Different combinations of these techniques have been tested against classifiers such as Multilayer Perceptron, Support Vector Machine and Radial Basis Function. Data fusion was investigated on mainly two levels namely feature-level and classifier-level with an aim of getting more reliable and robust results. Experimental results demonstrate that a classification accuracy of around 84% could be achieved by fusing data at the classifier level.