DC motors have wide applications in industrial control systems because they are easy to control and model. For analytical control system design and optimization, sometimes, a precise model of the DC motor used in a control system may be needed. In this case, the values of the motor parameters given in the motor specifications for reference, usually provided by the motor manufacturer may not be considered precise enough, especially for cheaper DC motors which tend to have relatively large tolerances in their electrical and mechanical parameters. General system identification methods proposed by Ljung (1999), Unbehauen and Rao (1998), Franklin, Powell, and Workman (1990), Basilio and Moreira (2004) can be alied to DC motor model identification. In particular, various methods have been applied to DC motor parameter identification, i.e., Mamani, Becedas, Sira-Ramirez, and Feliu-Batlle (2007), Mamani, Becedas, and Feliu-Batlle (2008), used the algebraic identification method, Krneta, Antic, and Stojanoviv (2005) used the recursive least square method, Hadef and Mekideche (2009) applied the inverse theory, Ruderman, Krettek, Hoffman, and Betran (2008) used the least square method, Hadef, Bourouina, and Mekideche (2009) applied the moments method. Identified DC motor models are often subsequently used for controller design and/or optimization, e.g. Ruderman, Krettek, Hoffman, and Betran (2008), Rubaai and Kotaru (2000), Mamani, Becedas, and Feliu-Batlle (2008).