End-to-End (E2E) approach, which maps a sequence of input features into a sequence of grapheme or words, to Automatic Speech Recognition (ASR) is a hot research agenda. It is interesting for less-resourced languages since it avoids the use of pronunciation dictionary, which is one of the major components in the traditional ASR systems. However, like any deep neural network (DNN) approaches, E2E is data greedy. This makes the application of E2E to less-resourced languages questionable. However, using data from other languages in a multilingual (ML) setup is being applied to solve the problem of data scarcity. We have, therefore, conducted ML E2E ASR experiments for four less-resourced Ethiopian languages using different language and acoustic modelling units. The results of our experiments show that relative Word Error Rate (WER) reductions (over the monolingual E2E systems) of up to 29.83% can be achieved by just using data of two related languages in E2E ASR system training. Moreover, we have also noticed that the use of data from less related languages also leads to E2E ASR performance improvement over the use of monolingual data.