The performance of a least mean square (LMS) adaptive filter with respect to the feedback factor μ in both stationary and nonstationary environments are outlined and investigated by simulations. A μ-vector LMS algorithm that is an extension of the conventional LMS algorithm is proposed. In the proposed algorithm, different values of feedback factor are used for different taps. Computer simulation results are given and it is proved that the authors' algorithm performs better than the conventional LMS algorithm in a nonstationary environment. Using the proposed algorithm, a two-stage adaptive system is suggested, the first stage of which is an adaptive line enhancer. The application of the method to biomedical signal processing is discussed.