Muscle electrical control has been extensively documented in the pursuit of methodologies to extract pertinent information for the artificial reproduction of natural movements. Nevertheless, the physiological phenomena underlying this process are complex. The system comprises a finite set of actuators, each responsible for generating electrical impulses that propagate throughout the muscular tissue. The use of superficial electrodes for signal acquisition introduces an additional layer of complexity due to cross-talk phenomena. Consequently, the precise positioning of electrodes is imperative to enhance the quality of the extracted information. In this study, we evaluate the impact of electrode placement on movement recognition rates using a quadratic discriminant classifier, as well as the influence of unintended electrode displacement as a determining factor. This investigation utilizes a high-definition open-access database. Root Mean Square (RMS) values were computed from measurements obtained from 128 electrodes, and a sequential feature selection (SFS) algorithm was employed to identify the optimal subset of features. Recognition rates were calculated for each participant and for the overall sample of 18 participants to derive intersubject and intrasubject results. Furthermore, three displacement scenarios were developed: longitudinal displacement, transverse displacement, and diagonal displacement, aligned with muscle fiber orientation. The results encompass evaluations using the top four to ten most significant features identified via SFS, the feature subset, and all electrode measurements. This study shows that electrode positioning significantly impacts movement classification, with random displacement (7.5–12.54 mm) causing variations up to 17.16% within and 24% between subjects. RMS values per electrode were analyzed using the 4 to 10 most relevant features, revealing variations of 10.92% and 9.6% (4 features) and 6.4% and 7.6% (10 features). Cross-validation was employed to ensure that results were independent of data partitioning, and ANOVA was used to confirm statistically significant differences between group means.