Try this: D1 = load('X.mat'); D2 = load('Y.mat'); X = D1.X; Y = D2.Y; data = [X,Y]; hh3 = hist3(data, 'Nbins',[1 1]*60); figure image(flipud(hh3)) ax = gca; xt = ax.XTick; yt = ax.YTick; ax.XTickLabel = xt*10; set(ax, 'YTick',[0 yt], 'YTickLabel', [flip([0 yt])]*10) producing: Experiment to get different results. .

You can get what you want with multiple calls to histogram2, one for each x value.

xs = 10:10:100;
ys = normrnd(50,20,numel(xs),5000);

colors = jet(numel(xs));
for i = 1:numel(xs)
    histogram2(repmat(xs(i),1,size(ys,2)),ys(i,:),'FaceColor',colors(i,:))
    hold on
end
hold off

histogram2() creates a bivariate histogram plot that you can apply to your data. Here's a demo that applies this plot to noisy gaussian curves. Pay attention the variables x and y which will be inputs to histogram2. Produce the noisy data. You'll already have the data; you just need to make sure it has the same general shape as these x and y variables. % Produce noise gaussian data % x is a 1xm or mx1 vector that defines the x values for each curve. % y is a mxn matrix of m y-values for n curves. gausFcn = @(X,C,A,S)exp(-(X - C).^2/(2*S^2)) * A; % guassian function (x, center, amp, sigma) x = 0 : 0.5 : 3000; % The x values for all curves nCurves = 20; % Number of curves to produce y = zeros(numel(x),nCurves); % We'll store the y data here % Produce n noisy curves for i = 1:nCurves y(:,i) = gausFcn(x,randi(100)+1200,(rand(1)+1)*4+10,randi(120)+500) + sin(linspace(0,randi(20)*pi,numel(x))); end % Show the noisy data figure() sph(1) = subplot(2,1,1); plot(x,y) grid on Apply the bivariate histogram % First, replicate the x values so there's 1 x value for each y value. xRep = repmat(x, 1, nCurves); % for row vectors; if col vec: repmat(x, nCurves, 1) % apply the bivariate historgram sph(2) = subplot(2,1,2); linkaxes(sph) h = histogram2(xRep(:),y(:),'DisplayStyle','tile','ShowEmptyBins','on'); Result Note, if you'd like it to look like the image you shared, set colormap to gray but reverse the order of colors. colormap(flipud(gray(255))) See the histogram2() link to learn how to set the x and y grid in the bivariate histogram.