Due to the inherent feedback in a Decision Feedback Equalizer (DFE) the Minimum Mean Square Error (MMSE) or Wiener solution is not known exactly. In this paper, the authors theoretically analyze a DFE taking into account the decision errors. They study its performance at steady state. They then study an LMS-DFE and show the proximity of LMS-DFE attractors to that of the optimal DFE Wiener filter (obtained after considering the decision errors) at high Signal to Noise Ratios (SNR). Further, via simulations they demonstrate that, even at moderate SNRs, an LMS-DFE is close to the MSE optimal DFE. Finally, they compare the LMS DFE attractors with IDFE via simulations. They show that an LMS equalizer outperforms the IDFE.