Product types in the hard disk drive (HDD) industry have different specifications depending on the customer orders. These specifications along with the machine parameters have a direct impact on the production yield. The problems on the manufacturing line are called the “root cause”. By accurately identifying the root cause, the engineers can suggest yield improvement solutions. Thus, the overall framework for the automatic solution generation is needed by several HDD companies. Our research is a part of such a framework. In this paper, we focus on the prediction technique required at the end of the analysis steps in order to validate the suggested solution by simulation. We adapt the Stochastic Neural Networks (SNNs) for using with the HGA yield prediction. The inputs of our model consist of several machine parameters and specification attributes. Our version of SNNs can approximate a complex non—linear system. The genetic algorithm is used as a learning algorithm instead of the back-propagation method in order to handle the non-linear and stochastic relationships between input parameters. Our prediction model can then be used to validate and revise the yield improvement plan. The output of the prediction model is the yield rate. The model can be used as a simulation tool for yield improvement without having to actually implement the solution on the production line.