This article analyzes the performance of a beamforming-based acoustic mapping model that uses signals captured by a wireless acoustic sensor network (WASN) when environmental and signal capture parameters change. The acoustic mapping process can be described in the stages of: distribution of the WASN in the acoustic environment, acoustic signal capture by the WASN nodes, and acoustic mapping model implementation. The analyzed factors are: processing time, shape of the map, and localization performance in the presence of several acoustic elements. The characterization presented in this work consists on the variation of parameters (such as number of proof points, window length, number of microphones, number of sources, type of sources, presence of noise, acoustic signal frequency composition, etc.) and analyzing its impact on three beamforming techniques that have been recently shown to be able to provide an acoustic map using signals captured by a WASN: Delay-and-Sum (DAS), Minimum Variance Distortionless Response (MVDR), and Phase-Based Binary Masking (PBM). As a result of this analysis, an optimal configuration for the acoustic mapping process can be defined for a selected application: MVDR was shown to be more robust against noise and array geometry, but was slower in processing time than PBM which, in turn, only provided better acoustic maps in specific non-noisy scenarios.