Subvocal electromyogram (EMG) signal classification is used to control a modified web browser interface. Recorded surface signals from the larynx and sublingual areas below the jaw are filtered and transformed into features using a complex dual quad tree wavelet transform. Feature sets for six subvocally pronounced control words, 10 digits, 17 vowel phonemes and 23 consonant phonemes are trained using a scaled conjugate gradient neural network. The subvocal signals are classified and used to initiate web browser queries through a matrix based alphabet coding scheme. Hyperlinks on web pages returned by the browser are numbered sequentially and queried using digits only. Classification methodology, accuracy, and feasibility for scale up to real world human machine interface tasks are discussed in the context of vowel and consonant recognition accuracy.