Many signals obtained from the real world, including the ones captured from the human body, present different events repeated recurrently. When these events possess some common features repeated in time, we call them “temporal patterns”. Examples of these temporal patterns can be the electrocardiogram waveform or the accelerations of the human body during the gait. This thesis proposes different techniques to model these temporal patterns and to detect their presence in a given signal. Due to the fact that these signals usually present a high variability in its shape and duration, we have applied different concepts and techniques from Soft Computing since they are able to deal with this variability. In general terms, two different approaches has been studied in this thesis: • Prediction-Error-Classification approach. It consists of generating a signal predictor for each pattern and using the errors produced by each predictor to determine the class of a given signal. • Fuzzy Finite Automata approach. The pattern is considered as a sequence of events which are modelled by an automaton. Afterwards, this automaton is used to detect patterns in a given signal. The performance of our proposed techniques has been tested on synthetic signals and on the analysis of the movements of human body.