An approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given measurement Z and two hypothesis H1 and H0 , we first estimate posterior probabilities P(H0\Z) and P(H1\Z); and choose the one with the larger posterior probability as the true hypothesis. Applications of the approach are illustrated with experiments devised to evaluate the performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented.