Dynamic Time Warping - Comparing Values
The result of DTW is a matrix $N\times M$, where $N$ is the size of the first MFCC-dataset in comparison and $M$ is the size of the second one. Now to make a decision of how close two datasets are to each other you need to see at the normalized bottom-right value value of the DTW matrix. If it's less than some pre-defined threshold, then you can say that two datasets are of a similar nature. In order to compute the threshold you need to run DTW multiple times and plot a ROC curve. The following sample DTW implementation in Matlab should help:

function cost = dtw (A, B)
  [rowsA colsA] = size (A);
  [rowsB colsB] = size (B);

  D = inf (rowsA + 1, rowsB + 1);
  D(1) = 0;

  for i = 2:rowsA + 1
    for j = 2:rowsB + 1
      d = norm ( A(i - 1, :) - B(j - 1, :) );
      D(i, j) = d + min ([ D(i - 1, j - 1) D(i - 1, j) D(i, j - 1) ]);
    endfor
  endfor

  cost = D(end) / (rowsA + rowsB);
endfunction