Handwritten signature plays its authorization role in most legal and financial documents. It is the most accepted and economical means of personnel authentication. It can be verified using online or offline verification schemes. This paper proposes a model to verify signatures by combining features like Zernike moments, circularity and aspect ratio. Unlike characters, signatures vary each time because of its behavioural biometric property. Signatures can be identified based on their shape. Moments are the good translational and scale invariant shape descriptors. The amplitude and phase of Zernike moments, circularity property and aspect ratio of the signature are the features that are extracted and fed to fuzzy classifier. Fuzzy logic classifies the signature into genuine or forged. Experimental results reveal that this methodology of combining zernike moments along with the two mentioned geometrical properties give higher accuracy and the accuracy rate increases with the increase in number of samples given to the fuzzy classifier. Keywords: Offline Signature Verification, Zernike Moments, Fuzzy Logic.