PID controllers and model-predictive controllers are widely used for process control. However, optimization of control parameters is complicated and many processes are operated inefficiently. In order to perform more effective control, we propose a new process control method using soft sensor models. We refer to this method as inverse soft sensor-based feed-forward (ISFF) control. Soft sensor models are constructed between a controlled variable (y) as an objective variable, and manipulated variables (U) and other process variables (X) as explanatory variables. The optimal control strategy of U which optimizes the objective function including y is determined with inverse analysis on the soft sensor models while considering X variables. The proposed method was applied to the change of a set point of a simulated CSTR system, and the validity of ISFF was confirmed.