Fingerprints are the oldest and most widely used form of biometric identification.  Everyone is known to have unique, immutable fingerprints. As most Automatic Fingerprint Recognition Systems are based on local ridge features known as minutiae, marking minutiae accurately and rejecting false ones is very important.  However, fingerprint images get degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction.  A critical step in automatic fingerprint matching is to reliably extract minutiae from the input fingerprint images. This paper proposes the classification of false minutiae for better matching results. The fake minutia are rejected on the basis of 3 most common cases.