The performance attributes of a broad class of randomised algorithms can be described by a recurrence relation of the form T( x) = a( x) + T( H( x)), where a is a function and H( x) is a random variable. For instance, T( x) may describe the running time of such an algorithm on a problem of size x. Then T( x) is a random variable, whose distribution depends on the distribution of H( x). To give high probability guarantees on the performance of such randomised algorithms, it suffices to obtain bounds on the tail of the distribution of T( x). Karp derived tight bounds on this tail distribution, when the distribution of H( x) satisfies certain restrictions. In this paper, we give a simple proof of bounds similar to that of Karp using standard tools from elementary probability theory, such as Markov's inequality, stochastic dominance and a variant of Chernoff bounds applicable to unbounded geometrically distributed variables. Further, we extend the results, showing that similar bounds hold under weaker restrictions on H( x). As an application, we derive performance bounds for an interesting class of algorithms that was outside the scope of the previous results.