End-to-end automatic speech recognition (ASR) approaches have emerged as a competitive alternative to traditional HMM-based ASR systems. Unfortunately, most end-to-end ASR systems are not easily reproduced since they require vast amounts of data and computational resources that are only available for a reduced set of companies and labs worldwide. Consequently, the performance of these systems is not very well known for low resource languages to the best of our knowledge. European Portuguese is one of those languages. In this work, we present a set of experiments to train and assess some of the most current successful end-to-end ASR approaches for European Portuguese. The proposed system, named TRIBUS, is a hybrid CTC-attention end-to-end ASR combining data from three different domains: read speech, broadcast news and telephone speech. For comparison purposes, we also train a state-of-the-art HMM-based baseline on the same data. Experimental results show that TRIBUS achieves 8.40% character error rate (CER) on the broadcast news test set without the need of a language model, which is comparable to the strong baseline result, 4.33% CER, on the same set using an in-domain language model. We consider this result quite promising, especially for highly unpredictable vocabulary ASR applications.