Prognostic aims at estimating the remaining useful life (RUL) of a degrading equipment, i.e at predicting the life time at which a component or a system will be unable to perform a desired function. This task is achieved through essential steps of data acquisition, feature extraction and selection, and prognostic modeling. This paper emphasizes on the selection phase and aims at showing that it should be performed according to the predictability of features: as there is no interest in retaining features that are hard to be predicted. Thereby, predictability is de ned and a feature selection procedure based on this concept is proposed. The e ectiveness of the approach is judged by applying it on a real-world case: through comparison is made in order to show that the better predictable features lead to better RUL estimation.