Any NDE process may be considered to involve three systems, each having a unique set of parameters that define its characteristics viz. (a) The Input to the material, (b) The material itself, and (c) The output response measured by the NDE system. Traditionally, the input and the material parameters are assumed known and numerous Forward Models have been developed that predict or estimate the output response function. Over the years, forward models are very well established and serve the key purpose, for improved interpretation of the, as well as to optimize the input parameters to obtain the desired, output response. The other two scenarios i.e. if the output response function in the form of measured data is available, to obtain one of system parameters, i.e. either the input function or the material properties, while the other one is assumed to be known are classified as Inverse Problems. Due to the availability of computational resources, the inverse problem solutions are becoming increasingly feasible. Typical applications include measurement of material properties such as modulus, viscosity, temperature, hardness and stress profiles, etc. This paper will discuss the different techniques and the kinds of problems that have been successfully addressed in the area of NDE and their implications on the expanding horizons in NDE.