In　this　article,　the　problem　of　estimating　the　covariance　matrix　in　general　linear　mixed　models　is　considered.　Two　new　classes　of　estimators　obtained　by　shrinking　the　eigenvalues　towards　the　origin　and　the　arithmetic　mean,　respectively,　are　proposed.　It　is　shown　that　these　new　estimators　dominate　the　unbiased　estimator　under　the　squared　error　loss　function.　Finally,　some　simulation　results　to　compare　the　performance　of　the　proposed　estimators　with　that　of　the　unbiased　estimator　are　reported.　The　simulation　results　indicate　that　these　new　shrinkage　estimators　provide　a　substantial　improvement　in　risk　under　most　situations.