The performance of decision-feedback equalization combined with maximum-likelihood detection (DFE/ML) using the fixed-delay-tree-search/decision feedback (FDTS/DF) algorithm was estimated analytically in terms of the length of the feedback-filter and the depth of the ML-detector. Performance degradation due to error propagation in the feedback-loop and in the ML-detector was taken into account by using a Markov process analysis. It was quantitatively shown that signal-to-noise-ratio (SNR) performance in high-density magnetic recording channels can be improved by combining an ML-detector with a feedback-filter and that the error propagation in the DFE channel can be reduced by using an ML-detector. Finally, it was found that near-optimum performance with regard to channel SNR and error propagation can be achieved, over the channel density range from 2 to 3, by increasing the sum of the feedback-filter length and the ML-detector depth to six bits.