Experimental methods capable of generating sets of co-regulated genes have become commonplace, however, recognizing the regulatory motifs responsible for this regulation remains difficult. As a result, computational detection of transcription factor binding sites in such data sets has been an active area of research. Most approaches have utilized either Gibbs sampling or greedy strategies to identify such elements in sets of sequences. These existing methods have varying degrees of success depending on the strength and length of the signals and the number of available sequences. We present a new deterministic iterative algorithm for regulatory element detection based on a Markov chain background. As in other methods, sequences in the entire genome and the training set are taken into account in order to discriminate against commonly occurring signals and produce patterns, which are significant in the training set.