S. Georgakarakos 1 , V. Trygonis 1 and J. Haralabous 2 ! " # ! $ Acoustic methods have been widely used in fisheries research for pelagic fish biomass estimation, lately including very sophisticated techniques, such us multi$frequency, wide band, multibeam, vertical and horizontal echosounding. Moreover, in the new era of the ecosystem$based management, developments in acoustic technology could extend our knowledge from the stock to the ecosystem (Bertrand, 2003) and enhance our understanding of the ecosystem structure (Koslow, 2009). Until now, biologists utilised acoustic technology mainly for fish biomass estimation. Normally, acoustics are superior to other methods for pelagic fish stock assessment (Simmonds, 2003); acoustic surveys are therefore often used to tune the VPA or other classical biomass estimation methods. The main advantage of fisheries acoustics is its ability to estimate most measurements errors and provide a level of the total accuracy of the abundance, which only crudely can be asserted by the other methods. This error analysis and de$biasing approach is not easy to design and less easier to implement in a real situation. However, it is worth to investigate each error factor affecting the measurement, estimate its nature (random or systematic) or its magnitude and try to minimise its impact if possible. Finally, a procedure known as intrinsic error analysis takes into account all errors sources and estimates the total error, hence revealing the quality of the final results. Detail studies of error analysis are recently published for krill (Demer, 2004) and Norwegian spring$spawning herring assessment (Løland et al 2007). The chapter will review some of the most important sources of errors and their impact on acoustic biomass estimation, with emphasis on the assessment of pelagic species and the development of methods aiming at relevant de$biasing approaches. Simmonds and MacLennan (2005) reviewed this problem and provided some indicators of how much error might be expected in a typical acoustic survey, with optimum sampling design and proper instrument preparation. The expected error magnitudes are reproduced in Table 1 slightly modified. The errors are divided in two groups, those generated due to the instrumentation uncertainty and others caused by the living resource complexity of behaviour. Absolute biomass estimations expressed in weight per unit area have a higher uncertainty compared to the estimates of relative indices, namely acoustic integration values per unit area. According to Table 1, large systematic errors such as these generated by bubble attenuation, hydrographic conditions and vessel avoidance can underestimate the biomass up to 10% of its original value. However, if the same research vessel is used under similar speed, weather