Texture is a spatial property and thus any features used to describe it must be calculated within a neighbourhood. This process of integrating information over a neighbourhood leads to what we will refer to as the texture boundary response problem, where an unwanted response is observed at object boundaries. This response is due to features being extracted from a mixture of textures and/or an intensity edge between objects. If segmentation is performed using these raw features this will lead to the generation of unwanted classes along object boundaries. To overcome this, post processing of feature images must be performed to remove this response before a classification algorithm can be applied. To date this problem has received little attention with no evaluation of the alternative solutions available in the literature of which we are aware. In this work we perform an evaluation of known solutions to the boundary response problem and discover separable median filtering to be the curre nt best choice. An in depth evaluation of the separable median filtering approach shows that it fails to remove certain parts or types of object boundary response. To overcome this failing we propose two alternative techniques which involve either post processing of the separable median filtered result or an alternative filtering technique.