Gesture recognition is defined as non-verbal motions used as a means of communication in Human Computer Interaction. It is one of the significant aspects of HCI, both in the device interfaces and interpersonally. In a virtual reality system, gestures can be used to navigate, control or interact with a computer. The aim of gesture recognition is to capture gestures that are formed in a certain way and are detected by a device such as a camera. Hand gesture recognition is one of the logical ways to generate a convenient and high adaptability interface between devices and users. In this paper, a system is created for hand gesture recognition using image processing tools, namely Wavelets Transform (WT), Empirical Mode Decomposition (EMD) methods, Artificial Neural Networks (ANN) and Convolutional Neural Network (CNN), for gesture classification. These methods are evaluated based on many factors such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood, receiver operating characteristic, area under roc curve and root mean square. Preliminary results indicate that WT had less execution time than EMD and CNN. CNN had the ability to extract distinct features and classify data accurately while EMD and WT were less effective. Hence, the classification accuracy is improved dramatically.