In this contribution a function-basedapproach,to on-line sig- nature verification is presented. An initial set of 8 time sequences ,is used; then first and second time derivates of each ,function are com- puted over these, so 24 time sequences are simultaneously considered. Avaluable,function normalization is applied as a previous stage to a continuous-density HMM-based complete ,signal modeling ,scheme ,of these 24 functions, so no derived statistical features are employed, fully exploiting in this manner,the HMM modeling,capabilities of the inher- ent time structure of the dynamic process. In the verification stage, scores are considered not as absolute but rather as relative values with respect to a reference population, permitting the use of a best-reference score-normalization technique. Results using MCYT_Signature sub- corpus on 50 clients are presented, attaining an outstanding best figure of0.35% EER for skilled forgeries, when signer-dependent thresholds are considered.