This paper addresses the problem of tracking random drifting parameters of a linear regression system. The asymptotic properties of several estimation algorithms in the limit of slow drift are studied. The basic tool is the central limit theorem for a class of stochastic difference equations established under weak conditions on disturbances and observations. The estimates of the rate of convergence obtained in the paper allow the asymptotically optimal algorithms to be developed.