An accurate on-line measurement of quality variables are essential for the successful monitoring and control tasks in chemical process operations. However, due to the measurement difficulties such as the large time delays, the soft sensor, an inferential model, for the target quality variable, has been widely used as an alternative for the physical sensors. Partial least-squares (PLS) was used to develop a soft sensor because it can handle the correlations among many variables. However, the successful applications of linear projection methods like PLS were limited to only the cases without strong nonlinearities. This paper proposes a design methodology to build a soft sensor for chemical processes that can handle the correlations among many process variables and nonlinearities based on smoothness concept. The method has been directly motivated by the locally weighted regression that estimates a regression surface through multivariate smoothing. The proposed method will be illustrated by comparisons with other familiar methods. The industrial case studies have shown that the proposed method gives a better or equal performances over other methods such as PLS, nonlinear PLS and artificial neural networks.