Sign language elevates the communication gap between special and ordinary people. It allows them to connect with society. Many studies have proposed image-based or sensor-based models for sign language whereas this study develops a sign language recognition (SLR) system for Italian sign language through Myo armband Electromyography (EMG) sensors only. The sensor keeps the subject comfortable during its utilization. In the proposed methodology, raw EMG from the Myo armband was denoised using an infinite impulse response (IIR) filter. A combination of 11 features was extracted from various domains after preprocessing of EMG signals. Subsequently, some prominent classifiers were trained and tested for accurate system design. The system has achieved the highest accuracy of 93.5% using the Linear Discriminant technique. The system authentication was performed on a 10-folds cross-validation