  The problem of classifying sonar signals from rocks and mines first studied
by Gorman and Sejnowski has become a benchmark against which many learning
algorithms have been tested. We show that both the training set and the test
set of this benchmark are linearly separable, although with different
hyperplanes. Moreover, the complete set of learning and test patterns together,
is also linearly separable. We give the weights that separate these sets, which
may be used to compare results found by other algorithms.
