This thesis presents design and implementation of a wireless EMG sensor, an algorithm is developed to classify finger positions into open or closed position. The design of wireless EMG sensor utilizes a custom built single channel, EMG signal amplification and conditioning circuit. This signal conditioning circuit converts the EMG signal with ~2mVpp to a 3.3Vpp signal and also filters any noise present in the signal. The signal is then digitized using an Arduino Fio board and wirelessly transmitted using an XBee transreceiver operating in the 2.4 GHz ISM band. The received signal is processed using a python program to detect the onset. Hierarchical Temporal Memory (HTM) is used for classification of the EMG signals. Implementation of classification is done using the NUPIC v1.7 toolset from Numenta Inc. A two layer HTM network is designed to consist of 4x2 nodes. It is trained using an EMG sample data set consisting of 200 samples each for closed and opened position of fingers respectively. The real time movement of a finger is captured by onset detection algorithm and fed to the HTM network for classification. The accuracy of the network was observed to be largely dependent on the size of training EMG sample vector, the number of nodes, number of layers in the network, and maximum groups allowed inside each node in the network. The network was tested with 50 samples each of open and close movement and the accuracy was observed to be 94%.