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Magnetic flow meters (magmeters) are instruments for measuring the velocity of flow in many industrial applications. The signal that comes from a magmeter is noisy and conventional approaches are often not effective enough in dealing with actual field noise. Furthermore, diagnostic functions are attracting increasing attention, due to the possibility of implementing them in an inexpensive and reliable manner in magmeter hardware. Neural networks have proven capabilities for both learning and data handling in noisy circumstances. In this paper, a novel approach based on wavelet neural networks is presented to attack these two objectives. The stability, accuracy and response time of the new approach has been tested, and found to be superior to conventional approaches.