As semi-solid forging (SSF) is compared with conventional easting such as gravity die-easting and squeeze casting, the product without inner defects can be obtained from semi-solid forming and globular microstructure as well. Generally speaking. SSF consists of reheating, forging, ejecting precesses. In the reheating process, the materials are heated up to the temperature between the solidus and liquidus line at which the materials exists in the form of liquid-solid mixture. The process variables such as reheating time, reheating temperature, reheating holding time, and induction heating power have much effect on the quality of the reheated billets. It is difficult to consider all the variables at the same time when predicting the quality. In this paper, Taguchi method, regression analysis and neural network were applied to analyze the relationship between processing conditions and solid fraction. A356 alloy was used for the present study, and the learning data were extracted by the reheating experiments. Results by neural network were on good agreement with those by experiment. Polynominal regression analysis was formulated by using the test data from neural network. Optimum processing condition was calculated to minimize the grain size, solid fraction standard deviation, otherwise, to maximize the specimen temperature average. In this time, discussion is liven about reheating process of row material and results are presented with regard to accurate process variables for proper solid fraction, specimen temperature and grain size.