The surface electromyography (sEMG) signal is used in the medical field for treating various diseases related to muscular conditions, as well as in other applications such as video games, gesture detection for smart devices, motion pattern recognition, and monitoring muscle activity in athletes. The proper acquisition, processing, and handling of this signal are important for data reliability. There are specialized devices that digitize the sEMG signal, but since there is no established standard resolution, the resolution varies from one device to another. Currently, semiconductor companies are marketing remarkable 24-bit data acquisition chips. This higher resolution should provide better diagnostics but also demand memory storage and bandwidth resources, which are limited for a wearable device to be practical and realizable. At any rate, it is important to ensure the accuracy of the data for applications requiring the use of the sEMG signal. This article delves into the real resolution used to develop sEMG-based applications by first investigating the open access sEMG database accuracy and comparing it with the claimed resolution. A methodology is proposed for resolution evaluation. Additionally, hand gesture evaluation was conducted using classification algorithms attempting to ascertain the suitability of using 24-bit versus a lower resolution performance. And finally, an investigation of the wireless transmission required for eight high-resolution sEMG channels is presented. Preliminary results of hand gesture evaluation demonstrated a better classification when using 24-bit resolution but only for an accuracy improvement of 0.44% to 1.6% over the 16-bit data resolution. Some of the conditions under which this high resolution may be relevant are identified.