In this paper, we present a speaker identification algorithm for a microphone array based on a first-order joint Hidden Markov Model (HMM) where the observations correspond to the angle of arrival of the speech and the speech spectrum. The goal of the research is to investigate whether including angle of arrival information improves the speaker identification error rates compared to an algorithm based on the speech spectrum only. The spectral model consists of a Gaussian Mixture Model (GMM) using Multiple Discriminant Analysis (MDA) coefficients, and the angle model includes a separate histogram for each participant. The convergence time of the joint HMM is improved by estimating the GMM for each of the meeting participants prior to the start of the meeting and initializing each participant’s spectral GMM in the joint HMM to the pretrained parameter values. The performance of the algorithm is analyzed from data collected during live meetings recorded using an eight element, circular microphone array. For meetings where the participants are stationary, the results show significant improvement over a single channel speaker ID algorithms based on spectrum only.