This paper describes a system using two complementary sorts of information issuing from a hidden Markov model (HMM) for online signature verification. At each point of the signature, 25 features are extracted. These features are normalized before training and testing in order to improve the performance of the system. This normalization is writer-dependent; it exploits only five genuine signatures used to train the writer HMM. A claimed identity is confirmed when the arithmetic mean of two similarity scores, obtained on an input signature, is higher than a threshold. The first score is related to the likelihood given by the HMM of the claimed identity; the second score is related to the segmentation given by such an HMM on the input signature. A personalized score normalization is also proposed before fusion. Our approach is evaluated on several online signature databases, such as BIOMET, PHILIPS, MCYT, and SVC2004, which were captured under different acquisition conditions. For the first time in signature verification, we show that the fusion of segmentation-based information generated by the HMM with likelihood-based information considerably improves the quality of the verification system. Finally, owing to our two-stage normalization (at the feature and score levels), we show that our system results in more stable client-score distributions across databases and in a better separation between the distributions of client and impostor scores.