Laser Wire Additive manufacturing (LWAM) requires a clear understanding of process parameters and their effects on the geometry and wider material properties of the parts produced to support the production of consistent, repeatable quality parts. Furthermore, its ability to capitalise on using novel alloys depends on efficient characterisation of optimum process parameters. In this work, a method for identifying the range of usable parameters is presented, which produces sufficient data to train Cascade Forward Neural Networks, which are capable of predicting process windows and basic LWAM track geometries for 316L stainless steel. The performance of these networks provides the foundation for further work to identify optimum process parameters and, through transfer learning, may reduce the experimental requirements for the process development of other alloys.