Embedded systems, as opposed to traditional computers, bring an incredible diversity. The number of devices manufactured is constantly increasing and each has a dedicated software, commonly known as firmware. Full firmware images are often delivered as multiple releases, correcting bugs and vulnerabilities, or adding new features. Unfortunately, there is no centralized or standardized firmware distribution mechanism. It is therefore difficult to track which vendor or device a firmware package belongs to, or to identify which firmware version is used in deployed embedded devices. At the same time, discovering devices that run vulnerable firmware packages on public and private networks is crucial to the security of those networks. In this paper, we address these problems with two different, yet complementary approaches: firmware classification and embedded web interface fingerprinting. We use supervised Machine Learning on a database subset of real world firmware files. For this, we first tell apart firmware images from other kind of files and then we classify firmware images per vendor or device type. Next, we fingerprint embedded web interfaces of both physical and emulated devices. This allows recognition of web-enabled devices connected to the network. In some cases, this complementary approach allows to logically link web-enabled online devices with the corresponding firmware package that is running on the devices. Finally, we test the firmware classification approach on 215 images with an accuracy of 93.5%, and the device fingerprinting approach on 31 web interfaces with 89.4% accuracy.