We present a new method for the identification of time-invariant multichannel autoregressive (AR) processes corrupted by additive white observation noise. The method is based on the Yule-Walker equations and identifies the autoregressive parameters from a finite set of measured data. The input signals to the underlying process are assumed to be unknown. An inverse filtering technique is used to estimate the AR parameters and the observation noise variance, simultaneously. The procedure is iterative. Computer simulation results that demonstrate the performance of the identification method are presented.