This　article　proposes　a　variable　selection　procedure　for　partially　linear　models　with　right-censored　data　via　penalized　least　squares.　We　apply　the　SCAD　penalty　to　select　significant　variables　and　estimate　unknown　parameters　simultaneously.　The　sampling　properties　for　the　proposed　procedure　are　investigated.　The　rate　of　convergence　and　the　asymptotic　normality　of　the　proposed　estimators　are　established.　Furthermore,　the　SCAD-penalized　estimators　of　the　nonzero　coefficients　are　shown　to　have　the　asymptotic　oracle　property.　In　addition,　an　iterative　algorithm　is　proposed　to　find　the　solution　of　the　penalized　least　squares.　Simulation　studies　are　conducted　to　examine　the　finite　sample　performance　of　the　proposed　method.