It looks fine,
auto.arima() tries many candidate models. One of them may have been dodgy.
auto.arima() algorithm follows Hyndman & Khandakar (2008) Automatic time series forecasting (pdf), although the OCSB test is a new development. The algorithm tries different versions of p, q, P and Q and chooses the one with the smallest AIC, AICc or BIC. The choice of criterion depends on the which parameters you pass to the function. For some versions of p, q, P and Q, it may not be able to fit a model and hence you get that warning. However, a "good one" is selected.
You should also make sure that you have enough data, at least four years.
Some important checks:
- Does the model make sense? For example, if you have monthly retails sales, you will probably expect a seasonal model to be fit.
- How well does it forecast out of sample?