We consider a regression problem where uncertainty affects to the dependent variable of the elements of the database. A model based on the standard epsilon-Support Vector Regression approach is given, where two hyperplanes need to be constructed to predict the interval-valued dependent variable. By using the Hausdorff distance to measure the error between predicted and real intervals, a convex quadratic optimization problem is obtained. Non-linear regressors are introduced via the use of kernels and several numerical experiments are performed to test our methodology.