Abstract： In order to monitor the non-Gaussian industrial process, a novel non-Gaussian information based JITL soft sensor model was proposed in this paper. First, the non-Gaussian dissimilarity measure selects the most relevant local modeling samples of JITL model. Then, an ICA-PLS regression method was established on the most relevant local samples for quality variable prediction. From the local relevant sample selection to the final regression model construction, the proposed method can efficiently extract the higher-order statistical information and is well suited for the non-Gaussian process quality prediction. Meanwhile, the proposed method can well cope with the changes in process characteristics as well as nonlinearity. The validity of the proposed method was verified on the sulfur recovery unit.