A common problem in model verification is to determine the values of model parameters that provide the best fit to measured data, based oil some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently nonconvex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. As the need for a User-interactive parameter estimation software, especially for identifying kinetic parameters, was needed; in this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES) has been developed in MATLAB enviromnent. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.