This　paper　employs　the　SCAD-penalized　least　squares　method　to　simultaneously　select　variables　and　estimate　the　coefficients　for　high-dimensional　covariate　adjusted　linear　regression　models.　The　distorted　variables　are　assumed　to　be　contaminated　with　a　multiplicative　factor　that　is　determined　by　the　value　of　an　unknown　function　of　an　observable　covariate.　The　authors　show　that　under　some　appropriate　conditions,　the　SCAD-penalized　least　squares　estimator　has　the　so　called　＂oracle　property＂.　In　addition,　the　authors　also　suggest　a　BIC　criterion　to　select　the　tuning　parameter,　and　show　that　BIC　criterion　is　able　to　identify　the　true　model　consistently　for　the　covariate　adjusted　linear　regression　models.　Simulation　studies　and　a　real　data　are　used　to　illustrate　the　efficiency　of　the　proposed　estimation　algorithm.