One of the main data resources used in many studies over the past two decades for spoken language understanding (SLU) research in spoken dialog systems is the airline travel information system (ATIS) corpus. Two primary tasks in SLU are intent determination (ID) and slot filling (SF). Recent studies reported error rates below 5% for both of these tasks employing discriminative machine learning techniques with the ATIS test set. While these low error rates may suggest that this task is close to being solved, further analysis reveals the continued utility of ATIS as a research corpus. In this paper, our goal is not experimenting with domain specific techniques or features which can help with the remaining SLU errors, but instead exploring methods to realize this utility via extensive error analysis. We conclude that even with such low error rates, ATIS test set still includes many unseen example categories and sequences, hence requires more data. Better yet, new annotated larger data sets from more complex tasks with realistic utterances can avoid over-tuning in terms of modeling and feature design. We believe that advancements in SLU can be achieved by having more naturally spoken data sets and employing more linguistically motivated features while preserving robustness due to speech recognition noise and variance due to natural language.