This　article　develops　empirical　inference　method　for　linear　transformation　models　with　randomly　right　censored　data.　An　empirical　likelihood　ratio　statistic　is　constructed　through　a　synthetic　data　approach　and　is　shown　to　have　a　limiting　weighted　chi-square　distribution.　For　inference　convenience,　an　adjusted　empirical　likelihood　ratio　statistic　is　proposed　on　base　of　the　former　one,　which　is　shown　to　have　a　limiting　standard　central　chi-square　distribution.　Confidence　regions　of　regression　parameters　are　then　constructed.　A　simulation　study　is　carried　out　to　investigate　the　performance　of　the　empirical　likelihood　method　and　the　adjusted　empirical　likelihood　method　proposed　in　this　article　compared　with　the　traditional　normal　approximation　method.　It　results　out　that　the　two　empirical　likelihood　methods　have　more　accurate　confidence　regions　and　better　coverage　probabilities　than　normal　approximation　method.