  A hidden Markov process is a well known concept in information theory and is
used for a vast range of applications such as speech recognition and error
correction. We bridge between two disciplines, experimental physics and
advanced algorithms, and propose to use a physically oriented hidden Markov
process as a new tool for analyzing experimental data. This tool enables one to
extract valuable information on physical parameters of complex systems. We
demonstrate the usefulness of this technique on low dimensional electronic
systems which exhibit time dependent resistance noise. This method is expected
to become a standard technique in experimental physics.
