  Classic statistical techniques (like the multi-dimensional likelihood and the
Fisher discriminant method) together with Multi-layer Perceptron and Learning
Vector Quantization Neural Networks have been systematically used in order to
find the best sensitivity when searching for $\nu_\mu \to \nu_{\tau}$
oscillations. We discovered that for a general direct $\nu_\tau$ appearance
search based on kinematic criteria: a) An optimal discrimination power is
obtained using only three variables ($E_{visible}$, $P_{T}^{miss}$ and
$\rho_{l}$) and their correlations. Increasing the number of variables (or
combinations of variables) only increases the complexity of the problem, but
does not result in a sensible change of the expected sensitivity. b) The
multi-layer perceptron approach offers the best performance. As an example to
assert numerically those points, we have considered the problem of $\nu_\tau$
appearance at the CNGS beam using a Liquid Argon TPC detector.
