This paper presents a new gesture recognition algorithm that uses different features obtained from MYO sensor. To preserve the spatial and temporal alignment for different features of each movement, Two Dimensional Principal Component Analysis 2DPCA is employed to obtain the dominant features by processing the obtained data in its 2D form. Canonical Correlation Analysis CCA is used to find a space where the projection of similar training/testing pairs become highly correlated. The testing sequences is matched to the training set that gives maximum correlation in the new space obtained by CCA. Two new data sets for common HCI applications (gaming and air writing) were collected at LIMU lab, Kyushu university and used to verify the efficiency of the proposed algorithm. Low processing complexity, efficient storage requirement, high accuracy and fast decision are factors that promotes our algorithm for real time implementation.