This thesis is a continuation of the author’s specialization project where the ultimate goal is to build an object recognition framework suitable for mobile devices in real world environments, where control over parameters such as illumination, distance, noise and availability of consistent network architectures are limited. Based on shortcomings related to object recognition performance and architectural issues the author’s goal was to increase the flexibility, usability and performance of the framework.Literature was reviewed on frameworks in order to discover useful techniques for development and documentation. Together with a re-introduction to the implemented recognition scheme an evaluation of the original framework artefact was performed with regards to the goals of this thesis. The results from the evaluation aided in finding an approach that balanced trade-offs between flexibility, usability, correctness and performance. By using proven framework development and documentation tactics from the literature study the author created a new iteration of the framework, improving upon the previous solution. The result is a stand alone artefact containing a hierarchy of software packages which divide functionality and offer customization using a combination of inheritance and components. The introduction of components hides domain knowledge and allows for easier reuse.In order to improve recognition performance and framework flexibility the author added external server support for image information extraction as well as support for the usage of different feature detectors and descriptor extractors. Because of time constraints the author did not test these new feature detectors and descriptor extractors suitability or performance. This testing can now be performed by the customer.In order to ensure proper correctness a lower bound on the image resolution is set at 600x600 pixels. Using properly built models correct recognition in about 90% of the cases is achievable. The added support for server side information extraction improves the object recognition performance by 42% in ideal conditions using the lower bound images. This improvement is still not enough to meet the performance criteria and combined with other issues results in the framework falling short of being ready to build production environment applications.