Palmprint is a new biomentric method to recoginze a person. The features in a palmprint include principal lines, wrinkles and ridges, etc. Line strcture feature, which includes princlpal lines and wrinkes, is one of the most popular methods in palmprint recognition. However, the line structure feature does not contain the thickness and width information of principal lines and wrinkles, which are very important to discrimiate paimprints. Ridges are very important to discrimiante paimprints. Ridges are not included in the structure feature either. So these methods cannot distinguish different palmprints with similar line structure. Furthermore, the line extraction is a difficult task. the fact that princlpal lines, wrinkles and ridges liave different resolutions monvates us to analyza the palmprint using multi-resolution analysts inethod. A novel palmprint feature, named wavelet energy features, Is defined employing waveiet, which is a powrful tool of multi-resolution analysis, in this paper. WEF can reffect the wavelet energy distribution of the principnl lines, weinktes and ridges in several different waveier decomposition level (scaie), so its ability to discriminate palms is very strong. Easiness to compute is another virtue of WEF. The very high recognition rats obtained in experiments shows the effect of the proposed method.