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Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries. This article focuses on the development of non-linear adaptive soft sensors for prediction of naphtha initial boiling point (IBP) and end boiling point (EBP) in crude distillation unit. In this work, adaptive inferential sensors with linear and non-linear local models are reported based on recursive just in time learning (JITL) approach. The different types of local models designed are locally weighted regression (LWR), multiple linear regression (MLR), partial least squares regression (PLS) and support vector regression (SVR). In addition to model development, the effect of relevant dataset size on model prediction accuracy and model computation time is also investigated. Results show that the JITL model based on support vector regression with iterative single data algorithm optimization (ISDA) local model (JITL-SVR:ISDA) yielded best prediction accuracy in reasonable computation time.