On statistical pattern recognition in independent component analysis mixture modelling A natural evolution of statistical signal processing in connection with the progressive increase in computational power has been exploiting higher order information. Thus high order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for non linear processing of data with complex non Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas such as image processing impact echo testing cultural heritage hypnograms analysis web mining and might therefore be employed to solve many different real world problems.