This paper considers the earliness and tardiness problem of sequencing a set of independent jobs on non-identical multi-machines, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. A coupled gradient network approach is employed to provide a shop scheduling analysis framework. The methodology is based on a penalty function approach used to construct the appropriate energy function and a gradient type network. The mathematical formulation of the problem is firstly presented and six coupled gradient networks are constructed to model the mixed nature of the problem. After the network architecture and the energy function were specified, the dynamics are defined by steepest gradient descent algorithm.