In this paper, an automatic aircraft target recognition (ATR) framework is presented, which is based on the high resolution range profiles (HRRP) of aircraft targets. This work is divided into two major parts. First, we consider the generation of the HRRP, which includes the modeling and simulation of radar cross section (RCS), the design of step frequency waveform (SFW), and IFFT processing for HRRP synthesis. In practice, a possible circular shift of the received HRRP relative to the template HRRPs in target library may exist. In such a situation, we resort to the statistical classification technique to develop an ATR algorithm, which begins with using the Neyman Pearson criterion to determine whether a target is present or not, under a constant false alarm rate constraint. Then the circular correlation is used to estimate possible circular range shift, as well as the unknown phase shift and attenuation. Moreover, we adopt the Gram-Schmidt orthogonalization (GSO) procedure to construct a signal space, and then project the received HRRP onto the signal space. Finally, the target classification can be done in terms of maximum a posteriori (MAP) decision rule. Simulation results are also included to demonstrate the feasibility of this approach.