Rhythmic classifications of languages generally class American English as a stress-timed language and languages such as Hindi or Tamil as syllable-timed. Indian English, a term encompassing both the varieties of English used as a second language in India and standardized Indian English, is generally considered to be syllable-timed due to influence from the L1 (ex. Gargesh 2008). However, some researchers (ex. Pingali 2009), citing a 1971 study, as well as recent studies (Krivokapic 2013, Fuchs 2012) cast doubt on this classification. This study aims to determine whether Tamil L1 Indian English is syllable-timed as compared to American English. One useful way of classifying languages in terms of their rhythmic structure is by using the Pairwise Variability Index (Low, Grabe & Nolan 2000). The PVI is a sequential measurement that classifies languages according to the duration of vowels and of intervals between vowels with a normalized speaking rate. Using this statistical method, Low, Grabe and Nolan (2000) show that stress-timed language speakers show considerably more variability in vowel durations than syllable-timed language speakers. The current study calculates durational variability in successive acoustic-phonetic intervals using Pairwise Variability Indices. This investigation differs from previous studies in that it is based on an interactional corpus which presents the advantage of containing spontaneous speech between an Indian English speaker and an American English speaker. The findings show that the durational variability is greater in Indian English than in American English. This result provides evidence that the variety of Indian English can be classified as tending towards syllable-timing as compared to American English. However, as Grabe (2002) notes, the PVI can vary between speakers and possibly even within an interaction. Thus, this project will also examine the potential accommodation of Indian English speakers towards a more stress-timed rhythm while speaking with American English speakers using statistical methods for the rhythmic classification of languages.