Laser Ablation – Inductively Coupled Plasma Mass Spectrometry (LA-ICPMS) is a surface-based technique used to quantify the chemical composition of a solid to its elemental and isotopic level. The output signal for each LA-shot corresponds to a set of time series, in intensities (counts-per-second, cps), that provides information on the quantity of each isotope. LA-ICPMS is widely used in biological sciences. For instance in fish ecology, it is used to analyze fish otoliths (ear stones) to obtain information on the fish’s life history (i.e., origin, migrations or exposure to contaminants). The experimental protocol for translating the actual output from LA-ICPMS into isotope concentration is long and complex. The first step is specially time consuming: the intensities obtained from each shot have to be reviewed one by one by an expert to eliminate procedural spikes and define the intervals that optimally represent (1) the background noise (blank) and (2) the background noise plus the signal (plateau). Here we propose a method to facilitate this first step using a trained neural network. The ELM was trained using cases previously processed to emulate the decisions of the expert. Our results showed that in comparison to the manual treatment the quality of the assessment with ELM was optimal for an automatic processing.