Synoptic weather typing depends on the method and its implementation. This paper demonstrates the utility of a 2-phase batch self-organising map procedure (CP2) in comparison with a procedure (CP1) consisting of T-mode principal component analysis followed by convergent K-means clustering, for classifying the synoptic weather types over east Australia. Four classifications were obtained for the 52-year NCEP/NCAR dataset, one from CP1 and three from CP2. These classifications were examined in terms of grouping quality, mean type maps, type frequencies, lifetime and transitions, and in relation to the Southern Oscillation Index (SOI). The results showed that the classifications derived from different procedures are inter-confirmative and capture a similar set of major synoptic situations influencing east Australia, each having counterparts in previous studies and conforming well to local synoptic experience. In particular, this study demonstrates that CP2 is a promising tool for the purpose of weather typing with a two-fold utility: CP2 can perform data projection (neighbourhood width > = 1) and provide a flexible means for visualising the broad distribution of the daily weather patterns in the dataset; and the procedure can also conduct cluster analysis (neighbourhood width = 0) and produce results equ ivalent to those from CP1. It was found that performing data clustering or data projection may affect the inference associated with type frequencies. While focusing on methodology, the analysis has also shown that the frequency of synoptic weather types fluctuates on the seasonal, interannual, and decadal scales. From the 1970s to 1995 there was a decline in the occurrence of a few weather types associated with an east-coast trough extending over New South Wales from the north, and a slight increase in the activity of some anticyclonic types. Significant correlations were also identified between SOI and frequencies of synoptic weather types on the annual and seasonal timescales.