Why use information theory methods in analysing multiparticle production processes? To illustrate this let us suppose that experiment measures some new distribution of secondaries. Immediately theoreticians rush to describe the new **data** and provide a number of models, which differ drastically in what concerns their assumptions, nevertheless they all do explain experimental results in a remarkable good way. What has happened? To answer this one has to introduce notion of information: **data** considered contain only limited amount of information and all models possess it as well. They differ therefore in what concerns additional redundant information they posses, which reflects not so much the state of the measured object but rather the state of minds of scientists proposing these models. To quantify this one has to resort to methods of information theory and introduce information entropy as a measure of information [1,2,3]. The problem one is facing can be formulated in following way. Suppose one has system S, depending on some variable x ∈ (x min , x max ), on which one performs finite number n measurements providing us with R 1,...,n results. The question is: how to obtain the most plausible (least biased) and model independent probability distribution, p(x), of variable x?