Automatic modulation recognition is an important part of communications electronic monitoring and surveillance systems where it is used for signal sorting and receiver switching. ' This thesis introduces a novel application of multivariate statistical techniques to the problem of automatic modulation classification. The classification technique uses modulation features derived from time-domain parameters of instantaneous signal envelope, frequency and phase. Principal component analysis (PCA) is employed for data reduction and multivariate analysis of variance (MANOVA) is used to investigate the data and to construct a discriminant function to enable the classification of modulation type. MANOVA is shown to offer advantages over the techniques already used for modulation recognition, even when simple features are used. The technique is used to construct a universal discriminator which is independent of the unknown signal to noise ratio (SNR) of the received signal. The universal discriminator is shown to extend the range of signal-to-noise ratios (SNRs) over which discrimination is possible, being effective over an SNR range of 0-4OdB. Development of discriminant functions using MANOVA is shown to be an extensible technique, capable of application to more complex problems. i