We describe some experiments in which acoustic noise from the flow of fluids in a counter-current heat exchanger is used to detect leaks of various sizes and locations. Methods of processing the data and using the coefficients in an autoregressive (AR) series to extract information about the faults are discussed. We also compare the results of three classification strategies applied to the data, namely quadratic discrimination, nearest neighbors and artificial neural networks.