In recent years, much effort has been devoted to the study of the Dynamic Matrix Control (DMC) model. Such a model is essentially a predictive controller that computes moves on manipulated variables to create changes in the output. In a wide range of applications, the coefficients (control transfer coefficients) of the input-output model are generally unknown. Thus, in order to design the desired predictive controller, the first important task is to identify these parameters. In this work, a recursive algorithm for the aforementioned task is developed. Some asymptotic properties of such an algorithm is obtained. It is shown that the algorithm is strongly consistent and a suitably scaled error sequence satisfies a functional invariance principle. The asymptotic normality is used to build up interval (confidence region) estimates. Moreover, a useful and easily implernentable stopping rule is also developed