Virtual sensing technology is crucial for high product quality and productivity in any industry. This review aims to clarify the trend of research and application of virtual sensing technology in process industries. After a brief survey, practical issues are clarified by introducing recent questionnaire survey results: 1) changes in process characteristics and operating conditions, 2) individual difference of equipment, and 3) reliability of soft-sensors. Since input variable selection is crucial for high estimation performance, conventional methods and new group-wise variable selection methods are introduced, and the usefulness of the group-wise variable selection methods is demonstrated through industrial case studies. Just-in-time (JIT) modeling is dealt with as a promising virtual sensing technology that can cope with changes in process characteristics as well as nonlinearity. Recent developments leading to successful industrial applications are introduced: correlation-based JIT (CoJIT) modeling and locally weighted regression (LWR), especially LW-PLS, with modified similarity measures. Manufacturing processes in different industries are quite different in appearance, but they have very similar problems from the viewpoint of quality issue. There remain practical issues requiring further research efforts to realize high-performance, maintenance-free virtual sensing technology.