With the increasing size and complexity of machines and vessels, the inverse problems of continuous bodies are becoming necessary. In this paper the possibility of using a Multilayer Perceptron Network trained with the Backpropagation Algorithm for identifying the critical speeds and support condition (or shape parameters) of orthotropic plates in the supersonic flow is studied. The plate model considered is a tapered rectangular plate, using a transfer matrix method, to estimate the changes in various modal parameters, caused by the shape parameters and support condition of plates. The basic idea is to train a neural network with simulated patterns of the relative changes in natural frequencies (eigenvalues) and corresponding support condition (or shape parameters) and critical speed of plates in order to recognize the behaviour of plate. Subjecting this neural network to un-learning values should imply information about the shape parameters and critical speeds. The training data are obtained by the transfer matrix method.

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