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Supplemental airbag safety restraint systems are an integral part of today's vehicle package. This inflatable restraint technology relies heavily on woven fabrics and particularly on knowledge pertaining to a fabric's permeability as a function of pressure drop, inflation temperature of the gas and fabric weave. While fabric permeability can be quantified by actual experimental measurements, the number and non-linearity of the variables involved make the experiments time and cost intensive. Moreover, interpolations within a given data set yield questionable results. For these reasons a feed-forward artificial neural network (ANN) technique was utilized to predict fabric permeability. This is an interpretive procedure. An ANN routine must first be trained. During this training the ANN is introduced to actual cause and effect patterns with adjustments being made by changes in weighting factors until the errors in the output variables are minimized.