Mossbauer spectroscopy is a useful technique for characterizing the va
lencies, electronic and magnetic states, coordination symmetries and s
ite occupancies of the cation. The Mossbauer parameters of isomer shif
t and quadrupole splitting are useful to distinguish paramagnetic ferr
ous and ferric iron in several substances, while the internal magnetic
field provides information on the crystallinity. In recent years arti
ficial neural networks have shown to be a powerful technique to solve
problems of pattern recognition of a mineral from its Mossbauer. spect
rum, Mossbauer parameters data bank, crystalline structure and magneti
c phases of soil from Mossbauer parameters. A computer software named
Mossbauer Effect Assistant has been developed. It uses learning vector
quantization neural network linked to a Mossbauer data bank that cont
ains Mossbauer parameters of isomer shift, quadrupole spliting, intern
al magnetic field and the references of the substances. The program id
entifies the substance under study and/or its crystalline structure wh
en fed with experimental Mossbauer parameters. It can also list the re
ferences from the literature by feeding the name of the substance or t
he author of the publication. Typical application of Mossbauer Effect
Assistant in iron-bearing materials Mossbauer spectroscopy is present
in user friendly Microsoft Windows environment.