We present and evaluate two ranking-and-selection procedures for use in steady-state simulation experiments when the goal is to find which among a finite number of alternative systems has the largest or smallest long-run average performance. Both procedures extend existing methods for independent and identically normally distributed observations to general stationary output processes, and both procedures are sequential.