In various applications from radar processing to mobile communication systems based on CDMA or OFDM, M-AR multichannel processes are often considered and may be combined with Kalman filtering. However, the estimations of the M-AR parameter matrices and the covariance matrices of the additive noise and the driving process from noisy ob-servations is a key issue to be addressed. In this paper, we propose to solve this problem as an errors-in-variables problem. Thus, the noisy observation autocorrelation matrix compensated by a specific diagonal block matrix and whose kernel is defined by the M-AR parameters matrices must be positive semi-definite. Hence, the parameter estimation con-sists in searching every diagonal block matrix that satisfies this property, in reiterating this search for a higher model order and then in extracting the solution that belongs to both sets. The proposed algorithm outperforms existing methods, especially for low Signal-to-Noise Ratio and when the variances of the additive noise are not necessarily the same on each channel