In this paper, we propose an iterative algorithm to perform model selection. This algorithm is a sequential technique that utilizes ideas from filtering theory. We found that the value of initial parameters in our method plays a key role in selecting models. We study the effect of those parameters, and propose guidelines on how to choose them. Finally, we explore and discuss extensions of the algorithm.