We present the work developed on off-line signature verification using hidden Markov models (HMM; Rabiner and Juang, 1986). Left-to-right (LR)-HMM is a well-known technique used by other biometric features, for instance, in speaker recognition and dynamic or on-line signature verification. Our goal here is to extend LR-HMMs to the field of static or off-line signature processing using results provided by image connectivity analysis, which separates images in connected components known as blobs, each one made up of a cluster of adjacent pixels of the same nature. The chain encoding of perimeter points for each blob obtained by this analysis is an ordered set of points in the space, clockwise around the perimeter of the blob. Two different ways of generating models are discussed, depending on the way that blobs provided by the connectivity analysis are ordered. In the first proposed method, blobs are ordered according to their perimeter length. In the second one, blobs are ordered in their natural reading order, i.e., from top to bottom and left to right. Finally, two LR-HMM models are trained using the parameters obtained by these techniques. Verification results corresponding to each technique are compared and some improvements are proposed.