If applying the Box-Jenkins methodology, I'd consider an ARIMA(0,0,0)(2,0,0) to be among my set of tentative models.
Justification: the ACF decays at seasonal lags and there are two (possibly three?) significant spikes in the PACF at seasonal lags; namely, six and twelve (and eighteen?). The ARIMA(0,0,0)(3,0,0) model would also be lined up for consideration, but by appeal to the principle of parsimony, I'd begin with the ARIMA(0,0,0)(2,0,0) model and go from there.
Ultimately, if you follow the Box-Jenkins methodology, you'll need to perform an iterative modelling procedure to decide upon a final model.
There are books dedicated solely to showing how to implement a Box-Jenkins ARIMA model selection strategy. For example, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases by Alan Pankratz does this, so you may find it to be a useful guide if you want a longer term solution to developing ARIMA modelling skills.
Alternatively, you could avoid the subjectivity (and difficulty!) of trying to select models based on interpreting ACF and PACFs. There are a various other methodologies that remove this subjective element of interpreting ACFs and PACFs, which include sequential testing and minimizing some information criteria. For more details, you could refer to Chapter 7 of Judge, Griffiths, Hill, and Lee (1980), The Theory and Practice of Econometrics.
Lastly, the output shown in the question appears to be from the EViews software(?). If you had chosen to do your analysis using the R software, you could use handy tools (directly, sans external interfaces) like the
forecast package, which allows automatic selection of an ARIMA model given a time-series via its