Manual fingerprint classification algorithms are very time consuming, and usually not accurate. Fast and accurate fingerprint classification is essential to each AFIS (Automatic Fingerprint Identification System). This paper investigates a fingerprint classification algorithm that reduces the complexity and costs associated with the fingerprint identification procedure. A new structural algorithm for classification of fingerprints is described. This algorithm is based on structural features: core and delta, and their orientation. The accuracy and speed of the proposed method is tested for a large number of fingerprint images with different initial qualities. The results are independent of image orientation and, show a significant classification performance.