This paper describes a machine vision algorithm for inspecting a crimpled terminal. The crimpled terminal is one of wire harness assemblies which transmit current or signals between a pair of electrical or electronic assemblies. The major defect considered is wire exposure on wire barrels. To detect the wire exposure, we develope a multi-layer perceptron in which three features extracted from the image of the crimpled terminal are used as input data. The three features are edginess, variance, and total number of valley points(TVP). The multi-layer neural network has been successfully tested on a number of real specimens collected from a wire-harness factory.