Surface Electromyography (sEMG) has applications in prosthetics, diagnostics and neuromuscular rehabilitation, and has been an increasing area of study. This study attempts to use a fully integrated smart textile band with electrical connecting tracks knitted with intarsia techniques to evaluate the quality of sEMG acquired by knitted textile electrodes. Myoelectric pattern recognition for motor volition and signal-to-noise ratio (SNR) were used to compare its sensing performance versus the conventional Ag-AgCl electrodes. Overall no significant differences were found between the textile and the Ag-AgCl electrodes in SNR and prediction accuracy obtained from pattern recognition classifiers. On average the textile electrodes produced a high prediction accuracy, >97% across all movements, which is equivalent to the accuracy obtained with conventional gel electrodes (Ag-AgCl). Furthermore the SNR for the Maximum Voluntary Contraction did not differ considerably between the textile and the Ag-AgCl electrodes.