Structural pattern recognition techniques are an efficient way to apply a pattern oriented data retrieval paradigm. Some techniques have already been implemented in the JET Analysis Cluster (JAC) by means of a general purpose tool (software application) to allow the identification of similar patterns (structural shapes) inside temporal evolution signals. Data retrieval methods are based on three essential aspects: feature extraction (to reduce signal dimensionality), the classification system (to index objects according to some criteria) and similarity measure (to compare how similar two objects are), but there is not a single solution or unique criterion to handle these key elements. This paper provides a new solution to the localization and extraction of similar patterns in time-series data. Alternative searches are proposed to objectively increase the recognition of similar patterns so as to achieve better results on the data retrieval. In the proposed approach, patterns are represented by string of characters. Looking for patterns means looking for characters. The recognition problem is translated into a character-matching problem. Thinner search strategies have been studied with excellent results in the detection of long subpatterns. Long subpatterns are not so easy to identify since even a single mismatch in one character can compromise similarity between two patterns. Identifying long patterns in a fast, fault tolerant and intelligent way is the aim of the analyzed strategies, formally based on statistical criteria and some aspects of probability theory.