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This paper introduces a fuzzy inference system (FIS)-based model for recognizing running conditions using data collected with a triaxial accelerometer. Specifically, data from three axes of a triaxial accelerometer were used as the input, and various running conditions (slow, medium and fast) were considered the output of the FIS. The MATLAB^® fuzzy toolbox, which includes processes such as fuzzification, sets of fuzzy rules, fuzzy inference engine and defuzzification, was used to model the system. Mamdani-type fuzzy modelling was selected for developing the FIS. The structure of the generated fuzzy inference system includes three fuzzy rules (using if-then) and an initial set of membership functions. The performance of the proposed FIS model was assessed using the root mean square error (RMSE), mean absolute error (MAE) and non-dimensional error index (NDEI), which were found to equal 0.059, 0.213 and …