In this report, a distributed neural network of coupled oscillators is applied to an industrial pattern recognition problem. 1"he network s'tems j?om the study of the neurophysiology of the olfactory system. It is" shown that the network serves as an as'sociative memory, which possesses chaotic dynamics. The problem addres'sed is machine recognition of industrial screws, bolts, etc. in simulated real time in accordance with tolerated deviations/rom manufacturing specifications. AJ?er preprocessing, inputs are represented as 1 × 64 binary vectors'. We show that our chaotic neural network can accomplish this pattern recognition task better than a standard Bayesian statis'tical method, a neural network bins O' autoassociator, a three-layerJOedforward network under back propagation learning, attd our earlier o!factory bulb model that relies on a Hopf bifurcation from equilibrium to limit cycle. The existence of the chaotic dynamics provides the network with its"capability to suppress noise and irrelevant information with respect to the recognition task. The collective effectiveness of the "'cell-assemblies'" and the threshold function of each individual channel enhance the quali(v of the network as an associative memoo,. The network classifies"an uninterrupted sequence of objects"at 200 ms of simulated real time Jor each object. It reliably distinguishes the unacceptable objects (i.e., 100% correct classification), which is a crucial requirement ,for this speci.fic application. The effectiveness of the chaotic dynamics may depend on the broad spectrum of the oscillations, which may jorce classification by spatial rather than temporal characteristics of the operation. Further s'tudy of this biologically derived model is"needed to determine whether its"chaotic dynamics rather than other as yet unidentified attributes is responsible" for the superior performance, and, if so, how it contributes to that end.