Channel estimation problem is a crucial state of the art research topic that has been addressed in the literature for the past decade. The importance of such research area emerged from the fact that telecommunication scholars are searching for a wireless telecommunication system that provides a minimum error performance yet can utilize the used spectrum efficiently. Channel estimation is a common practice in most of the wireless communication systems to give the receiver an indication about the channel conditions and hence will affect his decision in detecting the transmitted information This thesis will Compare Four methods for Channel estimation problems based in semi-blind approach for MU-MIMO systems; the Rank Revealing QR factorization RRQR, Least square method LS, CAPON method and the Eigenvector method. Performance comparison is based on the Minimum mean square error (MMSE). The introduced method in this thesis is the Eigenvector method, and it's presented in comparison to the first three methods. Compared to the RRQR the Eigenvector is based on a searching function similar to the MUSIC function with the addition of the Eigen values of the null space in the search function.