Combining different distance matrices or dissimilarity representations can often increase the performance of individual ones. In this work, we experimentally study on the performance of combining Euclidean distances and its relationship with the non-Euclideaness produced from combining Euclidean distances. The relationship between the degree of non-Euclideaness from combining Euclidean distances and the correlations between these Euclidean distances are also investigated in the experiments. From the results, we observe that combining dissimilarities computed with Euclidean distances usually performs better than combining dissimilarities computed with squared Euclidean distances. Also, the improvements are found to be highly related to the degree of non-Euclideaness. Moreover, the degree of non-Euclideaness is relatively large if two highly uncorrelated dissimilarity matrices are combined and the degree of non-Euclideaness remains lower if two dissimilarity matrices to be combined are more correlated.