Subsurface soil profiling is an essential step in a site investigation. The traditional methods for in situ
investigations, such as SPT borings and sampling, have been progressively replaced by CPT soundings since they
are fast, repeatable, economical and provide continuous parameters of the mechanical behaviour of the soils.
However, the derived CPT-based stratigraphy profiles might present noisy thin layers, and its soil type description
might not reflect a textural-based classification (i.e. Universal Soil Classification System, USCS). Thus, this paper
presents a straightforward artificial neural network (ANN) algorithm, to classify CPT soundings according to the
USCS. Data for training the model have been retrieved from SPT-CPT pairs collected after the 2011 Christchurch
earthquake in New Zealand. The application of the ANN to case studies show how the method is a cost-effective
and time-efficient approach, but more input parameters and data are needed for increasing its performance
