For hearing-impaired people, sign language is a common communication means just like spoken language to ordinary people. Because most of ordinary people cannot understand sign language, it's difficult for hearing-impaired community to participate in social activities. This paper proposes an effective method for sign language recognition with simple 3D trajectory information to break down the barriers between hearing-impaired and normal persons. First of all, the 3D trajectory captured from Kinect is preprocessed, and both the trajectories from the probe and galleries is normalized by the size of the signer. Then the trajectories are resampled evenly by a fast, easy, and usable interpolation algorithm. Before matching of two curves, the trajectory of the probe is aligned to the gallery trajectory and both aligned trajectories are transferred into frequency domain by DFT, and vectors linking the real part, imaginary part and amplitude are obtained. Finally, Euclidean distance between two trajectories in frequency domain is calculated to evaluate the dis-similarity of two trajectory vectors according to the minimized distance. The experimental results on a data set of 239 sign words show that the presented approach is effective to recognizing isolated words of Chinese sign language.