Physical phenomena exhibit time-varying behaviour for number of reasons, mainly due to the variation of internal and external operating conditions. There exists large number of really challenging applications of linear time-varying system identification in different areas. For instance, telecommunications, signal processing and automatic control are some of the areas of applications where linear time-varying system identification has been extensively used. We find tremendous applications of system identification and modeling in the fields like chemical process, socioeconomic system, electric systems, hydrology, aeronautics, seismology, ecology, transportation, mechanical, structural engineering, life sciences and many more. In this book, different approaches for the identification of linear time-varying systems such as optimized forgetting factor approach, Kalman filter approach, combination of basis function and forgetting factor approach, and wavelet based approach have been presented.