The continuously increasing computing power of Digital Signal Processing makes it now possible to efficiently implement Non-linear Algorithms for Signal Processing (NLSP). This book proposes a comprehensive review of Non-Linear Signal Processing Methods and the associated Parameter Estimation principles. The various existing approaches are considered: Classical descriptions (Hammerstein models, Volterra Equations ...), and more modern ones like Neural Network based ones, Wavelet Transform based decompositions, etc. The estimation of parameters is also considered: Classical Kalman Filter, Particle Filtering, and Self Learning Networks.