The unrivalled growth in e-commerce of animals and plants presents an unprecedented opportunity to monitor wildlife trade to inform conservation, biosecurity, and law enforcement efforts. Using the Internet to quantify the scale of the wildlife trade (volume, frequency) is a relatively recent and rapidly developing approach, which currently lacks an accessible framework for finding relevant websites and collecting data. Here, we present an accessible guide for internet-based wildlife trade surveillance, which uses a systematic method to automate data collection from relevant websites. The guide is easily adaptable to the multitude of trade-based contexts including different focal taxa or derived parts, and locations of interest. Furthermore, as wildlife trade on the Internet becomes more widespread, the ability to collect large amounts of data on traded wildlife will become possible and desirable. Using a case study where we monitor 53 websites, we demonstrate the capabilities and limitations of this kind of large-scale surveillance system. We collected over half a million unique listings in a year and estimate that it would take over two years for one person to clean every listing. We propose that the development of machine learning methods for automation of data collection and processing become a priority and be tested for a variety of different contexts of wildlife trade-related web data.