The papermaking process is a typical nonlinear process with multiple input-output variables, so it is difficult to construct an accurate model for the process. Data-based modeling techniques may be used to establish a reliable paper plant model. In particular, the LSSVM (least-squares support vector machine) can be used to create a highperformance papermaking process model based on operation data. In this paper, we present a paper plant model that can predict three key output variables (basis weight, ash content, moisture content) with four input variables (stock flow, filler flow, speed, steam pressure) using LSSVM. The proposed LSSVM model is compared with other data-based models (the ANN (artificial neural network) model and the state-space model). The LSSVM model turned out to exhibit better estimation performance compared to others.