A neural network based design methodology is described which generates an approximate optimal design when given the desired overall size of the component. Once the methodology is developed for a particular class of components, near optimal designs can be obtained with minimal computational resources by practicing engineers who have little or no training in formal design optimization methods. The technique developed here uses computationally simple neural networks to simulate the function of a more complex shape optimization capability. These networks serve as approximate mapping functions for the optimal design space. Near optimal values of the design variables, performance measures and the objective function are obtained as outputs from the networks. The only data that must be supplied by the engineer are the values of a few key dimensions which describe the overall size of the part. The methodology is demonstrated for the design of generic suspension components in which the key dimensions are the overall length and width of the parts. Networks were developed using optimal design data obtained from a mathematical programming based shape optimization capability. These networks are capable of designing parts with accuracy suitable to that needed for early, conceptual design studies.