There are three kinds of uncertainty: one due to randomness, another due to incomplete knowledge and a third one due to vague or imprecise knowledge. Sometimes two kinds of uncertainty occur at the same time, especially in random experiments based on imprecise data. To model randomness when the probability distribution related to an experiment is unknown, a non-parametric estimation method must be used, such as the Parzen window. Uncertainty in measurement originates imprecise data, traditionally modelled through probability concepts. However, as probability applies to random events but does not capture their imprecision, this sort of uncertainty is better represented by a fuzzy number, through the superior probability-possibility transformation. This thesis proposes a non-parametric estimation method based on Parzen window to estimate fuzzy probability from imprecise data.