Soft-sensors have been widely used for estimating product quality or other key variables, but their estimation performance deteriorate when the process characteristics change. To cope with such changes, recursive PLS and Just-In-Time (JIT) modeling have been developed. However, recursive PLS does not always function well when process characteristics change abruptly and JIT modeling does not always achieve the high-estimation performance. In the present work, a new method for constructing soft-sensors based on a JIT modeling technique is proposed. In the proposed method, referred to as correlation-based JIT modeling (CoJIT), the samples used for local modeling are selected on the basis of the correlation among measured variables and the model can adapt to changes in process characteristics. The usefulness of the proposed method is demonstrated through a case study of a CSTR process, in which catalyst deactivation and recovery are taken into account. In addition, its industrial application to a cracked gasoline fractionator is reported. (C) 2009 American Institute of Chemical Engineers AIChE J, 55: 1754-1765, 2009