The idea of using UAV’s (Unmanned Aerial Vehicles) in civilian applications has created new opportunities for companies dedicated to inspections, surveillance or aerial photography amongst others. Nevertheless, the main drawback for using this kind of vehicles in civilian applications is the enormous cost, lack of safety and emerging legislation. The reduction in the cost of sensors such as Global Positioning System receivers (GPS) or nonstrategic Inertial Measurement Units (IMU), the low cost of computer systems and the existence of inexpensive radio controlled helicopters have contributed to creating a market of small aerial vehicles within an acceptable range for a wide range of applications. On the other hand, the lack of safety is mainly caused by two main points: Mechanical and control robustness. The first one is due to the platform being used in building the UAV in order to reduce the cost of the system, which is usually a radio controlled helicopter that requires meticulous maintenance by experts. The second is due to the complexity of the helicopter dynamics since it is affected by variations in flying altitude, weather conditions and changes in vehicle’s configuration (for example: weight, payload or fuel quantity). These scenarios disrupt the modeling process and, consequently, affect the systematic development of control systems, resulting to tedious and critical heuristic adjustment procedures. Researchers around the World propose several modeling techniques or strategies for dynamic modeling of helicopters. Some works on helicopter modeling such as (Godbole et al or Mahony et al,2000), (Gavrilets et al, 2001), (Metler et al and La Civita et al, 2002), and (Castillo et al, 2007) show broad approaches that have been done in this field of engineering. The lack of an identification procedure in some cases and the reduced field of application in others, make sometimes difficult to use them. In this chapter, not only a modeling is described, but also the identification procedure that has been successfully tested. The proposed model has been defined by using a hybrid (analytical and heuristic) algorithm based on the knowledge of flight dynamic and by resolving some critical aspects by means of heuristic equations that allow real time simulations to be performed without convergence problems. Evolutionary algorithms have been used for identification of parameters. The proposed model has been validated in the different phases of the aircraft flight: hovering, lateral, longitudinal or vertical using a