In both small and large-scale investigations, a reliable short-cut procedure to estimate the approximate parameters is very useful for the successive rapid checking of different Kinetic Model (KM) structures for their adaptation to current process data. An improved quality of the initial parameter guess also improves the reliability and the convergence rate for a subsequent exact Nonlinear Least Squares (NLS) regression technique applied for fitting the final model. The recently proposed Modified Integral transformation Procedure (MIP) short-cut estimation method of Maria and Rippin (1995) [Computers and Chemical Engineering 19 (Supplement), S709-S714 (1995)] adds supplementary elements of similarity analysis and prior information about similar model structures to the classical Integral transformation Procedure (IF) for kinetic parameter estimation. By exploiting the model structure and the interactive use of information stored in a kinetic databank, the MIP makes rapid adaptation of a KM and parameters, describing an already studied process, to a similar process under study with only the product distribution known. The problem decomposition and the term-by-term sensitivity and estimation analysis of the model for various portions of experimental data sets result in a very effective MIP. The generated initial parameter estimate is more reliable and of better quality compared with the classical direct techniques, especially for non-linear and ill-conditioned cases. Algebraic transfer of information functions are developed in interaction with the kinetic databank, leading to a rapid check of different kinetics, or the same kinetic model for different data sets, without time-consuming intermediate NLS steps. The MIP was integrated in an expert system for kinetic identification and coupled with statistical data/estimate analysis (Maria, 1993 [Computers and Chemical Engineering 17 (Supplement), S435-S440 (1993)]; Maria and Rippin, 1996 [Computers and Chemical Engineering 20 (Supplement), S587-S592 (1996)]). MIP implies any iterative search, it has no convergence problems and requires no tuning factor. The basic MIP, developed for isothermal data treatment, is also shown to be suitable for on-line kinetics identification in (semi-) batch processes. The interaction with the prior information allows on-line adaptations of the model structure and parameters, comparable with extended Kalman Filter (EKF)-based recursive estimators. In the present work these results are also extrapolated for linear kinetics estimation by using non-isothermal data.