Abstract:The results of applying the default Automatic Model Identification of program TRAMO to a set of 15,642 socio-economic monthly series are analyzed. The series cover a wide variety of activities and indicators for a large number of countries, and the number of observations ranges between 60 and 600. The model considered by the automatic procedure is an ARIMA model with -when detectedoutliers and calendar effects. For series with no more than 360 observations the results are found satisfactory for slightly more than 90% of the series, excellent indeed as far as whitening of the series and the capture of seasonality are concerned. For longer series the normality assumption is the weak point. Still, in so far as kurtosis is the main cause, non-normality does not seem to be a dramatic feature. The relevance of including possible outliers and calendar effects is discussed in an Appendix.