Objectives: The aim of the present study is to investigate the usefulness of features extracted from miniature accelerometers attached to speaker's tracheal wall below the glottis for classification of phonation type. The performance of the accelerometer features is evaluated relative to features obtained from inverse filtered and radiated sound. While the former is a good proxy for the voice source, obtaining robust voice source features from the latter is considered difficult since it also contains information about the vocal tract filter. By contrast, the accelerometer signal is largely unaffected by the vocal tract and although it is shaped by subglottal resonances and the transfer properties of the neck tissue, these properties remain constant within a speaker. For this reason, we expect it to provide a better approximation of the voice source than the raw audio. We also investigate which aspects of the voice source are derivable from the accelerometer and microphone signals. Methods: Five trained singers (two females and three males) were recorded producing the syllable [pæ:] in three voice qualities (neutral, breathy and pressed) and at three pitch levels as determined by the participants’ personal preference. Features extracted from the three signals were used for classification of phonation type using a random forest classifier. In addition, accelerometer and microphone features with highest correlation with the voice source features were identified. Results: The three signals showed comparable classification error rates, with considerable differences across speakers both with respect to the overall performance and the importance of individual features. The speaker-specific differences notwithstanding, variation of phonation type had consistent effects on the voice source, accelerometer and audio signals. With regard to the voice source, AQ, NAQ, L1L2 and CQ all showed a monotonic variation along the breathy – neutral – pressed continuum. Several features were also found to vary systematically in the accelerometer and audio signals: HRF, L1L2 and CPPS (both the accelerometer and the audio), as well as the sound level (for the audio). The random forest analysis revealed that all of these features were also among the most important for the classification of voice quality. Conclusion: Both the accelerometer and the audio signals were found to discriminate between phonation types with an accuracy approaching that of the voice source. Thus, the accelerometer signal, which is largely uncontaminated by vocal tract resonances, offered no advantage over the signal collected with a normal microphone.