With recent progress in wearable sensing, it becomes reasonable for individuals to wear different sensors all day, and thus, global activity monitoring is establishing. The goals in global activity monitoring systems are among others to tell the type of activity that was performed, the duration and the intensity. With the information obtained this way, the individual’s daily routine can be described in detail. One of the strong motivations to achieve these goals comes from healthcare: To be able to tell if individuals were performing enough physical activity to maintain or even promote their health. This work focuses on the monitoring of aerobic activities and targets two main goals: To estimate the intensity of activities, and to identify basic/recommended physical activities and postures. For these purposes, a dataset with 8 subjects and 14 different activities was recorded, including the basic activities and postures, but also examples of household (ironing, vacuum cleaning), sports (playing soccer, rope jumping), and everyday activities (ascending and descending stairs). Data from 3 accelerometers—placed on lower arm, chest, and foot—and a heart rate monitor were analyzed. This paper presents the entire data processing chain, analyses and compares different classification techniques, concerning also their feasibility for portable online activity monitoring applications. Results are presented with different combinations of the sensors. For the intensity estimation task, using the sensor setup composed of the chest accelerometer and the HR-monitor is considered the most efficient, achieving a performance of 94.37 %. The overall performance on the activity recognition task, using all available sensors, is 90.65 % with boosted decision trees—the classifier achieving the best classification results within this work.