The United Nations System of Environmental and Economic Accounting-Ecosystem Accounting (SEEA EA) is a geospatial approach, whereby existing data on ecosystem stocks and flows are collated to show changes over time. The framework has been proposed as a means to track and monitor ecosystem restoration targets across the EU. Condition is a key consideration in the conservation assessment of habitats protected under the EU Habitats Directive and ecosystem condition accounts are also integral to the SEEA EA. While SEEA EA accounts have been developed at EU level for an array for ecosystem types, condition accounts remain the least developed. Collating available datasets under the SEEA EA framework, we developed extent and rudimentary condition accounts for peatland ecosystems at catchment scale in Ireland. Information relating to peatland ecosystem sub-types or habitat types was collated for peatland habitats listed under Annex I of the EU Habitats Directive, as well as degraded peatlands not included in EU nature conservation networks. While data relating to peatland condition were limited, understanding changes in ecosystem extent and incorporating knowledge of habitat types and degradation served as a proxy for ecosystem condition in the absence of more comprehensive data. This highlighted the importance of the ecosystem extent account, which underpins all other accounts in the SEEA EA framework. Reflecting findings at EU level, drainage, disturbance and land conversion were identified as the main pressures affecting peatland condition. We highlighted a number of options to gather data to build more robust, time-series extent and condition accounts for peatlands at varying accounting scales. Overall, despite the absence of comprehensive data, bringing information under the SEEA EA framework is considered a good starting point, with the integration of expert ecological opinion considered essential to ensure development of reliable accounts, particularly when working at ecosystem sub-type (habitat type) and catchment scale.