The use of hand gesture recognition has been steadily growing in various human-computer interaction applications. Under realistic operating conditions, it has been shown that hand gesture recognition systems exhibit recognition rate limitations when using a single sensor. Two dual-sensor approaches have thus been developed in this dissertation in order to improve the performance of hand gesture recognition under realistic operating conditions. The first approach involves the use of image pairs from a stereo camera setup by merging the image information from the left and right camera, while the second approach involves the use of a Kinect depth camera and an inertial sensor by fusing differing modality data within the framework of a hidden Markov model. The emphasis of this dissertation has been on system building and practical deployment. More specifically, the major contributions of the dissertation are: (a) improvement of hand gestures recognition rates when using a pair of images from a stereo camera compared to when using a single image by fusing the information from the left and right images in a complementary manner, and (b) improvement of hand gestures recognition rates when using a dual-modality sensor setup consisting of a Kinect depth camera and an inertial body sensor compared to the situations when each sensor is used individually on its own. Experimental results obtained indicate that the developed approaches generate higher recognition rates in different backgrounds and lighting conditions compared to the situations when an individual sensor is used. Both approaches are designed such that the entire recognition system runs in real-time on PC platform.