A radial basis functions neural network is trained to approximate speech spectrograms. We show that such approximations can be useful as a method of extracting known discriminatory features in speech patterns, using CV transition examples. We also argue that such an approximation to a joint time frequency representation can be seen as a description, of the dynamicsof speech patterns, that does not make uniform segmentation across different frequency bands.