This paper presents our investigations towards a non-invasive custom-built thought-to-speech converter that decodes mental tasks into morse code, text and then speech. The proposed system is aimed primarily at people who have lost their ability to communicate via conventional means. The investigations presented here are part of our greater search for an appropriate set of features, classifiers and mental tasks that would maximise classification accuracy in such a system. Here Autoregressive (AR) coefficients and Power Spectral Density (PSD) features have been classified using a Support Vector Machine (SVM). The classification accuracy was higher with AR features compared to PSD. In addition, the use of an SVM to classify the AR coefficients increased the classification rate by up to 16.3% compared to that reported in different work, where other classifiers were used. It was also observed that the combination of mental tasks for which highest classification was obtained varied from subject to subject; hence the mental tasks to be used should be carefully chosen to match each subject.