A novel approach for human silhouette recognition is presented. The method is based on Fourier descriptors. We made an analysis of which and how many descriptors are enough to have a general human silhouette representation, and concluded that a reduced number of components, low and high frequency, is sufficient for representing a human silhouette and for its recognition in different posses. Based on this study, we developed a system that uses 40 normalized descriptors and a nearest centroid classifier for human silhouette recognition. The method was tested with real images of humans and other objects with similar contours, achieving a 97% correct recognition.