In this paper, we describe a dynamic Bayesian network or DBN based approach to both two-hand gestures and onehand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by failsafe steps of motion tracking. Then a new gesture recognition model for a set of both one-hand and two-hand gestures is proposed based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to the model. In an experiment with ten isolated gestures, we obtained a recognition rate upwards of 99.59% with cross validation. The proposed model is believed to have a strong potential for successful applications to other related problems such as sign languages.