  Principal component analysis (PCA) algorithms use neural networks to extract
the eigenvectors of the correlation matrix from the data. However, if the
process is non-Gaussian, PCA algorithms or their higher order generalisations
provide only incomplete or misleading information on the statistical properties
of the data. To handle such situations we propose neural network algorithms,
with an hybrid (supervised and unsupervised) learning scheme, which constructs
the characteristic function of the probability distribution and the transition
functions of the stochastic process. Illustrative examples are presented, which
include Cauchy and Levy-type processes
