The cognitive load experienced by a person can be used as an index to monitor task performance. Hence, the ability to measure the cognitive load of a person using speech can potentially be very useful, especially in areas such as air traffic control systems. Current research on cognitive load does not provide enough insight into how cognitive load affects the speech spectrum, or the speech production system. Since formants are closely related to the underlying vocal tract configuration, this work aims to study the effect of cognitive load on vowel formant frequencies, and hence proposes the effective application of formant features to cognitive load classification. Results from classification performed on the Stroop test database show that formant features not only have lower dimensionality, but dynamic formant features can outperform conventionally used MFCC-based features by a relative improvement of 12%.