A target recognition method was proposed to recognize targets with different scales, view-points and illuminations automatically. First, a scale space of images was established, and the local key points in the scale space were extracted by incorporating the Hessian and Harris scale-space detectors. Then, the main orientations of the key points and orientation histograms were calculated and 128 element feature vectors for each key point were established, in which these feature vectors were invariant in different rotations and illuminants. To reinforce the performance, principle component analysis was incorporated to reduce the dimensionality of feature vectors and improve calculating speeds for the recognition. The nearest feature space classifier was used for increasing the recognition speeds in robustness. Experiment results show that this proposed method achieves a significant improvement in automatic target recognition rate, and the recognition rates for varied view-points, scales and illuminations are 61.9%, 80.5%, and 84.4%, respectively. Compared with the Scale Invariant Feature Transform(SIFT) and Speeded Up Robust Features (SURF), the proposed method achieves a significant improvement in automatic target recognition rate in presence of varying viewpoints, scales and illuminations.