Intra - user variability inherent in human handwritten signatures remains one of the main challenges for a robust biometrics signature based authentication system. The existence of a subset of users classified as 'goats' in the Doddington's menagerie whose signature samples are highly inconsistent and often rejected by the biometrics system may degrade the system accuracy by contributing a large portion to the False Rejection Rate (FRR). However, little is known on the level of the intra user variability and percentage of the 'goats' in the overall user population, which in turns remains the prime focus of this paper. An HMM-based computational approach is used to build the reference model and verifY the authenticity of an input sample based on a series of a local feature extracted from signature images. Here, four different goat populations are identified for offline signature biometric system which is based on four different local features ( namely pixel density, centre of gravity, angle, and distance) and are analysed for their co-relationship. The overall analysis is carried out on Sigma database which is compiled to reflect the signatures of a target user population.