The MIT Lincoln Laboratory submission for the 2004 NIST Speaker Recognition Evaluation (SRE) was built upon seven core systems using speaker information from short-term acoustics, pitch and duration prosodic behavior, and phoneme and word usage. These different levels of information were modeled and classified using Gaussian Mixture Models, Support Vector Machines and N-gram language models and were combined using a single layer perception fuser. The 2004 SRE used a new multi-lingual, multi-channel speech corpus that provided a challenging speaker detection task for the above systems. In this paper we describe the core systems used and provide an overview of their performance on the 2004 SRE detection tasks.