These images are then compared against known patterns which represent the various grades for a coin. The results have shown that the neural network recommendations are as accurate as the people-intensive grading method. Yet, by far the biggest use of neural networks as a recognizer of patterns is within the field known as quality control. A number of automated quality applications are now in use. These applications are designed to find that one in a hundred or one in a thousand part that is defective.
Human inspectors become fatigued or distracted.
Systems now evaluate solder joints, welds, cuttings, and glue applications. One car manufacturer is now even prototyping a system which evaluates the color of paints. This system digitizes pictures of new batches of paint to determine if they are the right shades. Another major area where neural networks are being built into pattern recognition systems is as processors for sensors. Sensors can provide so much data that the few meaningful pieces of information can become lost.