A composition tree, as obtained by the matrix group recognition project, can be used to build higher level algorithms, such as centralizer, normalizer, classes and subgroups. Some of these are straightforward generalizations of the permutation group algorithms, while others, in particular backtrack methods require larger changes. I will show what is possible and discuss how one could extend this to other representations of groups.