There is an option using matrices (interactive solution suggested by Jim is much easier to understand and was the first idea that come to my mind also)
Names Default To Here(1);
// This table shows how this will behave with ties
// dt = Open("$SAMPLE_DATA/2D Gaussian Process Example.jmp");
// cols_of_interest = {"X1", "X2"};
dt = Open("$SAMPLE_DATA/Pogo Jumps.jmp");
cols_of_interest = {"Yat", "Yee", "Sam"};
m = dt[0, cols_of_interest];
v = Shape(m, N Items(m));
r = Ranking(v); // let Ranking decide our top5
idx = Loc(r > N Items(m) - 5);
vals = J(1, N Items(m), .);
vals[idx] = 1;
vals = Shape(vals, N Items(m) / N Items(cols_of_interest));
For(i = 1, i <= N Cols(vals), i++,
rows_to_color = Loc(vals[0, i]);
Column(dt, cols_of_interest[i]) << Color Cells("Yellow", rows_to_color)
);
Write();
Pogo Jumps.jmp - Yat, Yee, Sam
2D Gaussian Process Example.jmp - X1, X2 (note the behavior with ties)
-Jarmo