Abundance indices based on nominal CPUE do not take into account confounding factors such as fishing strategy and environmental conditions, that can decouple any underlying abundance signal in the catch rate. As such, the assumption that CPUE is proportional to abundance is frequently violated. CPUE standardisation is one of the common analyses applied. The aims of this paper were to provide a statistical modelling framework for conducting CPUE standardisations using the Observer Program data for bigeye tuna, yellowfin tuna, albacore and southern bluefin tuna, and provide a comparison in the trends between the nominal CPUEs and their standardised indices obtained. The CPUE standardisations were conducted on the Observer Program collected between 2005 and 2007, by applying GLM analysis using the Tweedie distribution. The results suggested that year, area, HBF and bait factors significantly influenced the nominal CPUEs for the four tuna species of interest. Some extreme peaks and troughs in the nominal time series were smoothed in the standardised CPUE time series. The high degree of temporal variability that is still shown in the standardised CPUE trends suggests that the data are too sparse to give any meaningful indication of proxy abundance. Nevertheless, this may also suggest that variables used in the GLMs do not sufficiently account for all of the confounding factors, or abundance may indeed be truly variable.