In the last decade the so-called

${H}_{\infty}$ model reduction has received much attention becoming an important research topic in applied mathematics. Basically the reduction problem involves approximating a stable system with

$n$ states by another stable system with

$m<n$ states such that the associated model error satisfies a prescribed

${H}_{\infty}$ norm bound constraint. Most of the results have been derived in the context of continuous and discrete-time systems without delays and parameter uncertainties. The main aim of the present paper consists in studying the problem of

${H}_{\infty}$ model reduction for linear continuous time-delay systems. The case including systems with parameter uncertainties is analysed separately. In this respect, sufficient conditions based upon linear matrix inequalities and a coupling non-convex rank constraint are proposed in order to assure the existence of the desired reduced-order model. A simple illustrative example is also given to show the effectiveness of the proposed approach.