The responses of a PQQ-GDH entrapped in a polymer structure to mixtures of glucose and maltose were evaluated. Each compound was considered in the concentration range of 0–0.2 mM. Imaging was performed at constant height in the enzymatic feedback mode of scanning electrochemical microscopy (SECM). The enzyme-polymer spot was discretized into 15 × 15 μm2 substructures which were treated as independent individual microsensors. The response surfaces of the individual microsensors were approximated with a linear regression model. The coefficients in the derived equations represent contributions from topography, glucose concentration, maltose concentration, and the competition of glucose and maltose for the same active site of PQQ-GDH to the measured signal. The ratio of glucose and maltose contributions to the current at the SECM tip was constant for all microsensors and it was predominantly determined by the ratio of the turnover rates of both analytes in the PQQ-GDH catalyzed reaction. Using the difference between these coefficients, it was possible to select the microsensors within the overall enzyme-polymer spot that provided the best data for quantifying glucose and maltose by the artificial neural network used. The quantification of glucose and maltose was successful, except when the contributions from the components of the mixture were ng=kn units of glucose and simultaneously nm = 1.86(1−k)n units of maltose, for each constant n > 0 and k∈<0,1>.