We have developed a modeling system for the calculation processes of a process simulator for chemical vapor deposition (CVD). The system consists of a software agent, generalized modeling software and a simulator reproducing cross-sections of the deposited films. The agent autonomously creates appropriate models by operating the simulator and the modeling software. The models are calculated by partial least squares regression (PLS), quadratic PLS (QPLS) and error back propagation (BP) methods using artificial neural networks (ANN) and expresses by mathematical formulas to reproduce the calculated results of the simulator. In addition, we have investigated the performance of the models. The models show good reproducibility and predictability both for uniformity and filling properties of the films calculated by the simulator. The models using the BP method yield the best performance. The filling property data are more suitable to modeling than film uniformity. Since the simulator can be replaced by the models, the models will contribute towards decreasing the calculation costs for predicting the experimental results. This study thus presents an example of creating a robust and reliable predictor as a virtual reactor yielding experimental results that is expected to be free of the difficulties of extrapolation, using ANN and other mathematical models.