In this paper, we continue the discussion on database classifiers constructed with networks of interacting chemical oscillators. In our previous papers [1,2] we demonstrated that a small, regular network of oscillators can predict if three random numbers in the range [0, 1] describe a point located inside a sphere inscribed within the unit cube [0, 1] × [0, 1] × [0, 1] with the accuracy exceeding 80%. The parameters of the network were determined using evolutionary optimization. Here we apply the same technique to investigate if the classifier accuracy for this problem can be improved by selecting a specific geometry of interacting oscillators. We also address questions on the optimum size of the training database for evolutionary optimization and on the minimum size of the testing dataset for objective evaluation of classifier accuracy.