Identifying the underlying surface at varying speeds during robotic locomotion is important for safe and efficient robot navigation. This work aims to enhance the perceptual abilities of biped robots by enabling the force sensor affixed beneath each foot to recognize multiple indoor surfaces while navigating at varying speeds. An accurate yet cost-efficient surface-identification system for the robot is proposed by combining a real-time multi-object support vector machine (SVM) with an efficient time-domain feature. In this context, four promising hand-crafted time-domain features are investigated, among which the root mean square (RMS) feature is proven to outperform the other three features. A mean average precision (mAP) of 95.99% and 98.16% in 10-fold SVM cross-validation can be achieved by applying RMS at two different walking speeds, respectively. The high classification accuracy is achieved with high computing efficiency and thus enables system deployment on low-cost platforms such as Arduino or Jetson Nano, which makes our approach suitable for a wide range of applications across varying walking speeds.