  This paper describes a set of experiments with neural network classifiers on
the MNIST database of digits. The purpose is to investigate na\"ive
implementations of redundant architectures as a first step towards safe and
dependable machine learning. We report on a set of measurements using the MNIST
database which ultimately serve to underline the expected difficulties in using
NN classifiers in safe and dependable systems.
