Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the Wavelet Transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bi-spectrum and its bi-frequencies are proposed as effective features. Different types of Artificial Intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users.