This paper considers the problem of classifying human hand gestures by using electromyography (EMG) signals that are usually corrupted with noise. Noisy EMG signals result in significant degradation of classification performance and to enhance the performance, a Gaussian Smoothing Filter (GSF) is employed to remove the noise in the sensed EMG signals. The filtered signals, along with various classification schemes, are used to classify several hand gestures. The features of the GSF include: high filtering efficiency, simple implementation, and equal support in frequency and time domains, endowing the GSF with the ability to filter out the noise while partially retaining high frequency components of the original signal. The use of GSF produces smoothed EMG signals that not only enhances the classification accuracy but also reduces the computational time required to develop and test the classifiers. Experiments are conducted on EMG signals, captured from a MYO band, using multiple classification techniques and a significant improvement is observed in the classification performance when using the GSF to filter out the noise in the EMG signals. The classification performance for the EMG signals, for both unfiltered and filtered cases, is compared and the use of GSF is shown to yield significant performance enhancement. Moreover, a significant reduction in the computational time is reported when employing the GSF-based classification, demonstrating the advantages of the GSF for classifying EMG signals. Finally, a comparison is performed for classifying the EMG signals smoothed using a Median Filter (MF) versus the GSF and the superiority of the GSF is shown.