Turbulence deals with the complex motions in fluid at high velocity and/or involving a large range of length-scales. Understanding turbulence is challenging and involves many questions from modeling this complexity to measuring it. In this text, we aim at describing some tools of signal processing that have been used to study signals measured in turbulence experiments. Before that, another objective is the survey of some properties relevant for turbulent flows (experiments and/or models): scaling laws, self-similarity, multifractality and non-stationarity, that will explain why those techniques are useful.