The objects for analysis in multi-parametric combined patterns recognition systems are represented by several patterns with different nature of origin. This allows to increase the number of informative signs and the classification reliability. However, there is a negative aspect – increasing the time complexity of the data analyze. The aim of the study is reducing the time complexity of data analysis and decision making in multi-parametric combined pattern recognition systems. Well known solutions to this problem: data processing algorithms complication, boosting algorithms using and install more productive computing systems. However, more promising is use an approach that takes into account signs informativity level and decision-made occur does not across the entire data set. For achieve the aim of the study is being solved important scientific problem – developing the new method separate analysis of information signs in multi-parametric combined patterns recognition systems. This method is based on the fact, that in the multi-parametric combined patterns recognition systems the object is represented by a set of patterns which have a different nature of origin. Therefore, in the case of correct classification, all patterns from the totality allow to relate the object to the same class. The patterns of recognition object are compared until one or several of them is made a decision on the classification. In such an approach a full analysis of all signs is not carried out, which reduces the time complexity of the recognition process and, as a result, the classification decision-making process speed up