A small target detection approach which is based on principal component of hyperspectral imagery was presented. As a kind of multivariate data, the points of hyperspectral data always compose a hyper-plane in high-dimensional space. The principal component analysis can estimate the intrinsic dimensionality of the hyper-plane.Usually, the significant components contain most information of imagery. The insignificant components, which covers important details such as target characteristic, represent the information of orthogonal subspace. These insignificant principle components were used to detect small targets. This method reduces the dependent of the spectral pre-information and improves the practicability.