Constructing a sophisticated experiential knowledge base for solving daily problems is essential for many intelligent human centric applications. A key issue is to convert natural language instructions into a form that can be searched semantically or processed by computer programs. This paper presents a methodology for automatically detecting actionable clauses in how-to instructions. In particular, this paper focuses on processing non-imperative clauses to elicit implicit instructions or commands. Based on some dominant linguistic styles in how-to instructions, we formulate the problem of detecting actionable clauses using linguistic features including syntactic and modal characteristics. The experimental results show that the features we have extracted are very promising in detecting actionable non-imperative clauses. This algorithm makes it possible to extract complete action sequences to a structural format for problem solving tasks.